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https://grand-challenge.org/challenges/ | Challenges - Grand Challenge Grand Challenge Challenges Algorithms Reader Studies Blog Archives About Statistics Terms of Service Privacy Policy Help Grand Challenge Support Grand Challenge Documentation Sign In Register Challenges Here is an overview over the medical image analysis challenges that have been hosted on Grand Challenge. Please fill in this form if you would like to host your own challenge. Host your own Challenge Filter Challenges Title or Description Modality CBCT CT CT/MR Dermoscopy Endoscopy Fundus Photograp Histology Infrared Reflect Mammography MR OCT PET PET/CT PET/MR Transmission Ele US XR Anatomical Structure Colon (Abdomen) Kidney (Abdomen) Liver (Abdomen) Pancreas (Abdomen) Spleen (Abdomen) Heart (Cardiac) Brain (Head and Neck) Cranium (Head and Neck) Retina (Head and Neck) Teeth (Head and Neck) Knee (Lower Limb) Cervix (Pelvis) Prostate (Pelvis) Skin (Skin) Spinal Cord (Spine) Vertebral Column (Spine) Breast (Thorax) Lung (Thorax) Hand (Upper Limb) Anatomical Region Abdomen Cardiac Head and Neck Lower Limb Pelvis Skin Spine Thorax Upper Limb Organization Diagnostic Image Analysis Group QIA FME qurAI Radboud AI for Health Institute of Cancer Research Northwestern University Medical Deep Learning Lab Zeiss AWS Delft Imaging FastMRI ScreenPoint Medical HESGE Breast Imaging Group Radboudumc Challenge Series AAPM CAMELYON COMPAY ECDP ICAIR ICIP ISBI MICCAI MIDL OpenCXR RSNA SPIE Task Type Classification Detection Generalization Localization Modeling Prediction Reconstruction Registration Regression Segmentation Tracking Challenge status --------- Accepting submissions now Opening submissions soon Not accepting submissions Completed 241 challenges found AI4Life Calcium Imaging Denoising Challenge 2025 Algorithm submission challenge Accepting submissions 46 26 2025 CHIMERA Algorithm submission challenge Accepting submissions 314 297 2025 DataChallenge 2024 Algorithm submission challenge Challenge completed 3 5 Article Article Article 2025 UNICORN Algorithm submission challenge Challenge completed 307 844 2024 BEETLE Accepting submissions for BEETLE 78 13 Article 2025 Mitosis Domain Generalization Challenge 2025 Algorithm submission challenge Challenge completed 157 107 Article 2025 HANCOTHON Algorithm submission challenge Challenge completed 74 21 Article 2025 SSL for 3D light-sheet microscopy image segmentation Algorithm submission challenge Challenge completed 59 41 2025 RARE25 Algorithm submission challenge Challenge completed 226 323 2025 ToothFairy3: Multi-Class Segmentation in CBCT Volumes Algorithm submission challenge Accepting submissions for Task 1 - Multi-class Segmentation: Post Challenge Phase 183 97 2025 Surgical Visual Understanding Algorithm submission challenge Challenge completed 87 186 2025 TopBrain Segmentation Challenge for Whole Brain Vessel Anatomy Algorithm submission challenge Challenge completed 227 75 2025 The Trauma THOMPSON Challenge 2025 Algorithm submission challenge Challenge completed 71 54 2025 Dehazing Echocardiography Challenge 2025 Algorithm submission challenge Challenge completed 184 133 2025 CURVAS-Pancreatic Adenocarcinoma Vascular Invasion Algorithm submission challenge Challenge completed 62 65 2025 REport Generation in pathology using Pan-Asia Giga-pixel WSIs Challenge completed 410 2025 MICCAI Grand Challenge Tutorial 2025 Not accepting submissions 4 2025 ODELIA BREAST MRI Challenge 2025 Algorithm submission challenge Accepting submissions 132 36 Article 2025 autoPET/CT IV Algorithm submission challenge Challenge completed 191 42 2025 PANTHER Challenge Algorithm submission challenge Challenge completed 290 220 2025 AI4Life Microscopy Supervised Denoising Challenge 2025 Algorithm submission challenge Challenge completed 18 29 2025 Deep-learning Evaluation for Enhanced Prognostics - PSMA PET Algorithm submission challenge Challenge completed 136 27 2025 HEad and neCK TumOR Lesion Segmentation, Diagnosis and Prognosis Algorithm submission challenge Not accepting submissions 467 165 2025 Beyond FA Algorithm submission challenge Accepting submissions for Phase 1: Trial Images 84 6 2025 Fuse My Cells: From Single View to Fused Multiview Lightsheet Im Algorithm submission challenge Challenge completed 136 37 2025 The LUNA25 Challenge Algorithm submission challenge Challenge completed 1,101 558 Article 2024 TrackRad: Real-time tumor tracking for MRI-guided radiotherapy Algorithm submission challenge Accepting submissions for Post-challenge until Dec 30 2026 at 23:00 158 119 2024 2nd BONBID-HIE Challenge for HIE Outcome Prediction and Lesion S Algorithm submission challenge Challenge completed 140 37 2024 PUMA: Panoptic segmentation of nUclei and tissue in MelanomA Algorithm submission challenge Accepting submissions 416 245 Article 2024 MONKEY challenge: Detection of inflammation in kidney biopsies Algorithm submission challenge Accepting submissions 579 104 2024 Debug DRAGON Algorithm submission challenge Accepting submissions 1 3 2024 Calibration and Uncertainty for multiRater Volume Assessment in Algorithm submission challenge Challenge completed 83 60 2024 Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation Algorithm submission challenge Challenge completed 338 88 2024 Self-supervised learning for 3D light-sheet microscopy image seg Algorithm submission challenge Challenge completed 109 57 2024 SynthRAD2025 Algorithm submission challenge Accepting submissions 788 173 2025 TopCoW 2024 Challenge Algorithm submission challenge Challenge completed 201 280 Article 2024 Multi-Class Segmentation of Aortic Branches and Zones in CTA Algorithm submission challenge Challenge completed 133 157 2024 Pelvic Bone Fragments with Injuries Segmentation Challenge Algorithm submission challenge Challenge completed 272 197 2024 ToothFairy2: Multi-Structure Segmentation in CBCT Volumes Algorithm submission challenge Accepting submissions for Post Challenge Phase: Test Your Algorithm until Sep 30 2028 at 22:00 287 60 2024 Surgical Visual Understanding Algorithm submission challenge Challenge completed 90 225 2024 Previous Page 1 of 7 Next × Grand Challenge About Challenge Policy & Pricing Support & Documentation Statistics Status Policies Terms of Service Privacy Policy Developers API Documentation API Schema Developer Documentation Sponsors © 2012-2026 | 2026-01-13T09:29:11 |
https://www.stifel.com/investor-relations | Stifel Stifel Wealth Tracker App- Your Accounts, Tools, Research OPEN Skip to Main Content Stifel Wealth Tracker Login ID Password Forgot password? Organize Your Financial Life Pull all of your financial information into one singular, secure view Create a personal balance sheet Take a more in-depth look at your holdings Track your spending and view your net worth Better understand the companies you're invested in by accessing Stifel's nationally recognized research and investment insights Sign Up Learn more at stifel.com/tracker Learn more at stifel.com/tracker Download Stifel Wealth Tracker Log In Investor Relations Careers Advisor Opportunities Contact Us Find a Stifel Advisor About Us Individuals Institutions Banking & Lending Log In Menu Find a Stifel Advisor About Us Individuals Institutions Banking & Lending Investor Relations Careers Advisor Opportunities Contact Us Investor Relations An entrepreneurial culture built on sound financial management, continuous innovation, and long-term thinking Stifel Financial Corp. (NYSE:SF) 131.56 +0.00 (0.00%) Range 0.00 - 0.00 52 week 73.27 - 134.74 Open 0.00 Volume 106 Market cap. 13.40B Market data is delayed by at least 15 minutes. Stifel Financial Corp. is a diversified financial services holding company that conducts its business through several wholly owned subsidiaries. Its primary broker-dealer subsidiary, Stifel, Nicolaus & Company, Incorporated, is a full-service brokerage and investment banking firm established in 1890. Stifel Financial Corp. was formed as a holding company in July 1983 and is publicly traded on the New York Stock Exchange under the symbol "SF." Contact Investor Relations Contact Media Relations Contact Investor Relations x First Name Last Name Phone E-mail Message Please note: For security purposes, do not include the following in any e-mail correspondence: Account Numbers Client Account User IDs Passwords Social Security Numbers In addition, Stifel cannot accept the following via e-mail: Purchase or sale orders Instructions for the transfer or distribution of funds Change of address Please contact your Financial Advisor regarding any of the above. 2025 December 18, 2025 Stifel Reports November 2025 Operating Data “Total client assets and fee-based client assets reached record highs in November, up 8% and 14% year-over-year, driven by strong recruiting and market gains. Client money market and insured product balances increased 1% from October as growth in Sweep balances more than offset a decline in Smart Rate. Additionally, the impressive growth in Treasury deposits accelerated in November as balances increased 9% from the prior month and nearly doubled from a year ago. In our Institutional Group, client activity levels remain elevated, and despite the impact of the government shutdown, we anticipate a strong fourth quarter.” - Ron Kruszewski Chairman and CEO Press Release November 20, 2025 Stifel Reports October 2025 Operating Data “Total client assets and fee-based client assets reached record highs, increasing 12% and 18% year-over-year, respectively, driven by market appreciation and strong net asset inflows. Treasury deposits grew 35% over the prior year, reflecting the effectiveness of our venture and fund banking deposit initiatives, essentially offsetting declines in client money market and insured product balances. Bank loans rose 1% since September, supported by higher residential, securities-based, and commercial lending.” - Ron Kruszewski Chairman and CEO Press Release November 13, 2025 Stifel To Present at the Wolfe Research Wealth Symposium Press Release Webcast Slides October 27, 2025 Stifel Independent Advisors to be Acquired by Equitable “This transaction reinforces Stifel’s unwavering commitment to our core employee-channel advisory business, while ensuring that our independent advisors continue to thrive with an excellent partner that shares our values. We expect that intensifying our focus on employee advisors will help advance our goal of growing assets under management from $500 billion to $1 trillion and strengthen Stifel’s position as a premier wealth management firm.” - Ron Kruszewski Chairman and CEO Press Release October 22, 2025 Stifel Reports Third Quarter 2025 Results “Our third-quarter results once again highlight the strength of Stifel’s balanced business model and disciplined execution. We delivered record net revenue of more than $1.4 billion and $1.95 in earnings per share, the third highest in our history, driven by record results in Global Wealth Management and a 34% increase in Institutional revenue. As we enter year-end, I’m optimistic about the opportunities ahead. With record investment banking pipelines, record client assets, and an integrated wealth and banking platform that continues to gain momentum, Stifel is well positioned to build on its success.” - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides Webcast September 25, 2025 Stifel Reports August 2025 Operating Data “Total client assets and fee-based client assets hit record highs, up 9% and 14% year-over-year, driven by strong recruiting and market gains. The benefit of our diversified funding mix was illustrated by the continued growth in treasury deposits, up 6% month-over-month, which effectively offset a 1% decline in client money market and insured product balances that was primarily due to lower Smart Rate balances. Momentum is building in our Institutional Group as the capital raising and M&A environment has improved significantly since earlier this year. Barring a significant market downturn, we anticipate a stronger second half of the year for this segment.” - Ron Kruszewski Chairman and CEO Press Release August 28, 2025 Stifel Reports July 2025 Operating Data “Total client assets and fee-based client assets increased 9% and 14% year-over-year, respectively, to record levels. This strong momentum reflects both ongoing market appreciation and continued success in attracting net new assets. Treasury deposits surged 87% compared to last year, highlighting the strength of our venture and fund deposit gathering capabilities. This significant growth more than offset modest declines in client money market and insured product balances during July and underscores the resilience of our deposit base. Bank loans rose 1% in July, driven by increased Fund Banking and Residential lending.” - Ron Kruszewski Chairman and CEO Press Release August 13, 2025 Stifel to Meet With Institutional Investors at the J.P. Morgan Future of Financials Forum Presentation July 30, 2025 Stifel Reports Second Quarter 2025 Results "We achieved the best second quarter in our history, generating over $1.28 billion in net revenue and $1.71 in core EPS despite a challenging April. These results reflect the strength of our diversified, advice-driven model and the consistency of our performance across market cycles. With our strongest financial advisor recruiting quarter in 10 years, growing momentum in our Institutional business, and continued progress in key growth areas, we are confident about the second half of the year and beyond.” - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides Webcast June 26, 2025 Stifel Reports May 2025 Operating Data “In May, recruiting and market appreciation drove a 3% increase in total client assets and a 4% increase in fee-based assets. Client money market and insured product levels decreased less than 1% during the month primarily due to lower Smart Rate balances as Sweep balances experienced a slight decline. Investment banking activity was negatively impacted by increased market volatility in April, but we have seen momentum increase and our pipelines build throughout the quarter as markets have stabilized. As a result, we anticipate investment banking revenue in the quarter to be down approximately 10% from the second quarter of 2024 but we remain cautiously optimistic for the full year 2025.” - Ron Kruszewski Chairman and CEO Press Release June 03, 2025 Stifel to Meet With Institutional Investors at our Cross Sector Insights Conference Presentation May 22, 2025 Stifel Reports April 2025 Operating Data “Total client assets and fee-based assets increased 7% and 11%, respectively, from the same period a year ago, due to market appreciation and our continued success in recruiting productive financial advisors. On a month-on-month basis, both our total client assets and fee-based assets finished relatively in-line with March levels, despite significant volatility in the equities markets. Client money market and insured product balances decreased 5% in April as both Smart Rate and Sweep deposits were negatively impacted by typical seasonality.” - Ron Kruszewski Chairman and CEO Press Release April 23, 2025 Stifel Reports First Quarter 2025 Results "Our net revenue of $1.26 billion marks the highest first-quarter revenue in our history, with year-over-year growth across all revenue lines. The investments we’ve made in our business and our focus on delivering valued advice drove growth in both our Global Wealth Management and Institutional Group — despite the headwinds from market volatility and a significant legal charge. We remain optimistic about long-term growth, emphasizing the resilience of U.S. financial markets and the value our advice-driven model delivers during periods of uncertainty." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides Webcast March 27, 2025 Stifel Reports February 2025 Operating Data “Total client assets under management increased 11% in February to $506 billion and fee-based client assets rose 14% to $196 billion from the same period a year ago. Our growth continues to be driven by stronger equity markets and the addition of highly productive financial advisors. Client money market and insured products declined less than 1% from January, as modest increases in Sweep deposits were more than offset by lower Smart Rate balances. Despite our strong investment banking pipelines, market uncertainty and volatility in the quarter have negatively impacted activity levels. As such, we anticipate that our first quarter 2025 investment banking revenue will be similar to our first quarter 2024 results.” - Ron Kruszewski Chairman and CEO Press Release February 27, 2025 Stifel Reports January 2025 Operating Data “In January, client assets under administration reached $510 billion and fee-based assets grew to $197 billion, marking a 14% and 18% increase year-on-year. This growth was driven by stronger markets and a solid recruiting pipeline. Additionally, client money market and insured products rose 7% from the same period last year but the expected seasonal decline in Sweep deposits resulted in a 4% decrease during January.” - Ron Kruszewski Chairman and CEO Press Release February 10, 2025 Stifel to Meet With Institutional Investors Presentation January 29, 2025 Stifel Reports Fourth Quarter and Full Year 2024 Results "Stifel generated record net revenue and the second highest earnings per share in our history in 2024. The fact that we accomplished this level of performance in a year when our Institutional segment was rebounding from a very difficult operating environment in 2023 is a testament to the strength and diversity of our business model. Given our long history of profitable growth, Stifel is well positioned to capitalize on improving market conditions in 2025 and to achieve our short and long term targets." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides Webcast 2024 December 19, 2024 Stifel Reports November 2024 Operating Data "In November, total client assets under management climbed to a record $514 billion, including a record $197 billion in fee-based assets. This represents a 4% increase from the prior month, driven by strong equity markets and financial advisor recruiting. Client money market and insured product balances increased 3% from October as growth in sweep deposits and Smart Rate balances were comparable. The operating environment for our Institutional Group continues to improve as both client activity levels and investment banking pipelines increased.” - Ron Kruszewski Chairman and CEO Press Release November 21, 2024 Stifel Reports October 2024 Operating Data "Total client assets and fee-based assets increased 22% and 28%, respectively, from the same period a year ago, as we benefitted from strong market appreciation and our recruiting efforts. However, both metrics declined 1%, versus September 2024 levels, due to market declines. We continue to see positive trends in client cash levels, as client money market and insured product balances increased 2% in October, driven by growth in both Smart Rate and Sweep balances.” - Ron Kruszewski Chairman and CEO Press Release November 12, 2024 Stifel to Meet With Institutional Investors Presentation October 23, 2024 Stifel Reports Third Quarter 2024 Results "The third quarter represented our second highest quarterly net revenue, an increase of 17%, while earnings per share increased 150%. Through the first three quarters of 2024, net revenue was up 13% to a record $3.6 billion, driven by continued growth in Global Wealth, improvement in our Institutional business, and the stabilization of net interest income. Our financial results illustrate the strength of the Stifel franchise and our ability to capitalize on improving market conditions. Momentum in our business continues to build and we anticipate further upside to both the top and bottom lines in the fourth quarter and in 2025." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides September 26, 2024 Stifel Reports August 2024 Operating Data "Total client assets and fee-based assets increased 15% and 20%, respectively, from the same period a year ago, due to solid recruiting and market appreciation. Client money market and insured product balances increased 1% month-on-month, driven by growth in both Smart Rate and Sweep balances as cash sorting further slows. We continue to anticipate stronger investment banking revenue in the second half of the year versus the first half as activity levels increase. However, both the timing of deal closings and market volatility continue to impact revenue." - Ron Kruszewski Chairman and CEO Press Release September 05, 2024 Stifel to Meet With Institutional Investors Presentation August 22, 2024 Stifel Reports July 2024 Operating Data "Client assets under administration and fee-based client assets increased 2% from the prior month and reached record levels of $481 billion and $183 billion, respectively. The increases were driven by continued market appreciation and solid recruiting. Client money market and insured product grew roughly $100 million in July. Higher Smart Rate balances more than offset the decline in Sweep balances, as cash sorting continues to slow. Bank loans grew 2% in July, driven by increased Fund Banking, C&I, and Residential Mortgage activity." - Ron Kruszewski Chairman and CEO Press Release July 24, 2024 Stifel Reports Second Quarter 2024 Results “Stifel generated our second highest quarterly net revenue in company history, as each of our operating segments generated solid year-on-year gains. Stifel’s strong results reflect improved market conditions and illustrate the benefits of our balanced businesses and inherent operating leverage, particularly in our Institutional Group. Given current market trends, we believe that Stifel is well positioned for a strong second half of 2024.” - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides June 27, 2024 Stifel Reports May 2024 Operating Data "Recruiting and market appreciation drove a 3% increase in total client and fee-based assets during the month. Cash sorting slowed as client money market and insured product levels were essentially flat. Modest growth in Smart Rate balances essentially offset declines in Sweep balances. We continue to see improvement in the environment for investment banking activity, but the timing of closings for M&A transactions remains difficult to forecast." - Ron Kruszewski Chairman and CEO Press Release June 04, 2024 Stifel to Meet With Institutional Investors at our Cross Sector Insights Conference Presentation May 23, 2024 Stifel Reports April 2024 Operating Data “Total client assets and fee-based assets increased 12% and 15%, respectively, from the same period a year ago, as a result of strong recruiting and market appreciation. However, a decline in equity markets in April was the primary driver behind a 3% decline in both metrics versus March 2024 levels. Client money market and insured product balances decreased 2% in April, driven by seasonal tax payments and modest cash sorting as both Smart Rate and Sweep balances declined during the month. As we noted in our most recent earnings call, we are beginning to see increased lending opportunities and, as such, our total bank loans increased 2% in April, fueled by growth in our fund banking business.” - Ron Kruszewski Chairman and CEO Press Release April 24, 2024 Stifel Reports First Quarter 2024 Results "Total net revenue of more than $1.16 billion was our second highest quarterly net revenue ever, as the momentum we highlighted at the end of 2023 carried into the first quarter. Record Global Wealth Management revenue and improving market conditions for our Institutional Group drove our top and bottom line growth from the same period a year ago. While the outlook for the remainder of 2024 is not without risk, I remain cautiously optimistic that our diversified business will continue to generate strong returns for this year and beyond." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides March 28, 2024 Stifel Reports February 2024 Operating Data "In February, total client assets under management climbed to a record $458 billion, including a record $172 billion in fee-based assets. This represents a three percent increase from the prior month, driven by strong equity markets and financial advisor recruiting. Client money market and insured products increased 1% from January, despite modest seasonal cash sorting. While the environment for investment banking continues to improve, delays in deal closings remain a headwind for the business." - Ron Kruszewski Chairman and CEO Press Release February 27, 2024 Stifel to Meet With Institutional Investors at the UBS Financial Services Conference February 27, 2024 Presentation February 22, 2024 Stifel Reports January 2024 Operating Data "Client assets under administration and fee-based assets increased 1% from the prior month and reached record levels of $447 billion and $167 billion, respectively, as we continue to benefit from strong equity markets and solid recruiting pipelines. Client money market and insured products declined by 1% from year-end levels as the expected seasonal decline in sweep deposits was partially offset by the increase in Smart Rate balances. Overall client cash, inclusive of money market funds and short-term treasuries, was essentially flat in January compared to the prior month." - Ron Kruszewski Chairman and CEO Press Release January 24, 2024 Stifel Reports Fourth Quarter and Full Year 2023 Results "Stifel’s strong 2023 results underscore the importance of our diversified business model as the operating environment was less than ideal. Given our position as a premier wealth management firm and middle market investment bank, as well as the increased scale of our business, we see significant opportunities for top and bottom line growth as market conditions improve." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides 2023 December 21, 2023 Stifel Reports November 2023 Operating Data "In November, client money market and insured balances increased by more than 1% from October’s levels, as cash sorting activity continues to slow. Total client assets increased by 6% and total fee-based assets increased by 7% from the prior month, driven by strong equity and fixed income markets, as well as continued recruiting activity. Institutional Group revenues have improved from third quarter levels as investment banking activity increased from the prior quarter. Delays in M&A closings, however, continue to impact revenue." - Ron Kruszewski Chairman and CEO Press Release November 21, 2023 Stifel Reports October 2023 Operating Data “Total client and fee-based assets declined from the prior month’s levels due primarily to lower equity markets despite continued strength in recruiting. Client money market and insured product balances decreased 1% as growth in Smart Rate Balances was offset by declines in Sweep Balances. Overall, total client cash levels increased modestly from September due to growth in money market funds and short-term treasury balances.” - Ron Kruszewski Chairman and CEO Press Release November 08, 2023 Stifel to Present at the Wolfe Research Wealth Symposium November 8, 2023, 3:40 p.m. Eastern New York, New York Slides October 25, 2023 Stifel Reports Third Quarter 2023 Results “Stifel generated a solid quarter despite challenging market conditions. Our bottom line was impacted by non-recurring legal accruals, primarily associated with an industry-wide SEC review of off-channel communications that totaled $0.58 per diluted common share, after tax. Excluding the impact of these charges, our results are consistent with those in the sequential and year-ago quarters. Although the near-term environment remains uncertain, we remain well positioned to generate stable returns and strong growth as the market improves.” - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides September 28, 2023 Stifel Reports August 2023 Operating Data "Client cash increased by 2% in August as incremental deposits into our Smart Rate program increased, while cash sorting activity remained subdued. Additionally, Sweep and Smart Rate balances in September have increased from end of August levels. We continued to see strong financial advisor recruiting in August, which helped to partially offset the impact of lower equity markets on total client assets. In our institutional business, investment banking pipelines are improving but market conditions have delayed both deal announcements and closings." - Ron Kruszewski Chairman and CEO Press Release August 24, 2023 Stifel Reports July 2023 Operating Data "Total client assets increased in July, primarily as a result of higher equity markets. Recruiting activity was very strong as we added a number of highly productive advisors. Stifel continues to benefit from the investments we’ve made in our business as well as our recent ranking as No. 1 in employee advisor satisfaction among investment firms* in the J.D. Power 2023 U.S. Financial Advisor Satisfaction Study?. Total client cash fell less than 1%, as cash sorting continued to slow, and we have seen this trend continue so far in August." - Ron Kruszewski Chairman and CEO Press Release *Disclaimer July 26, 2023 Stifel Reports Second Quarter 2023 Results "Our results in the quarter reflect the benefits of Stifel’s diverse business model. We were able to leverage another record quarter in our Global Wealth Management segment to partially offset the continued market challenges faced by our Institutional Group. Although the outlook remains uncertain, we are beginning to see signs of improvement as our equity capital markets revenue improved both sequentially and year-on-year and we anticipate a pickup in advisory revenue in the second half of the year. As markets normalize, I expect that Stifel will capitalize on its increased scale and continue the firm’s long history of profitable growth." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides June 22, 2023 Stifel Reports May 2023 Operating Data "In May, total deposits increased while client cash balances were relatively flat with April’s levels. Through the first two months of the quarter, we experienced consistent growth in Smart Rate balances and have seen cash sorting continue to slow during June. Total client assets declined as solid recruiting was offset by declines in equity markets. Operating conditions for investment banking and our transactional businesses remain challenging. As such, we estimate that second quarter investment banking and transactional revenue will be down 10-15% and 5-10%, respectively, from the prior quarter." - Ron Kruszewski Chairman and CEO Press Release May 24, 2023 Stifel Reports April 2023 Operating Data "Total client assets increased modestly over the prior month due primarily to the benefits of higher equity markets. Total client cash fell 3% in April driven by seasonal tax payments. We continued to see the benefits of our client cash initiatives as both Smart Rate & and money market mutual fund balances increased in April. Additionally, cash sorting activity has slowed in May. Our institutional business continues to face headwinds as decreased volatility has pressured the equity business and investment banking activity remains subdued." - Ron Kruszewski Chairman and CEO Press Release May 24, 2023 Stifel to Meet With Institutional Investors at the UBS Wealth & Asset Management Conference May 24, 2023 Presentation April 26, 2023 Stifel Reports First Quarter 2023 Results "Stifel had a strong quarter led by record results in Global Wealth Management. More importantly, the quality of our franchise was on display as our balance sheet did not face the same issues that plagued many regional banks. Although the market outlook remains uncertain, I am confident that the diversity of our business model and our conservative approach will enable Stifel to continue to generate strong results in ever changing market conditions." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides March 23, 2023 Stifel Reports February 2023 Operating Data "In February, we continued to see strong net new asset growth from our existing clients and solid recruiting activity, however our total client assets decreased modestly due to declines in the equity markets. Operating conditions for investment banking have remained challenging, as such, we estimate that first quarter investment banking revenue will be down 5-10% from fourth quarter levels. In light of the recent turmoil in the banking industry, I’d like to reassure our clients and investors that Stifel’s balance sheet remains strong and liquid. While our client cash balances declined modestly due to seasonality and cash sorting in February, over the past two weeks, we have attracted over $1.3 billion in additional bank deposits, including an increase in uninsured deposits that reflects our strength in liquidity, short duration balance sheet, and strong capital position. Even accounting for the $1.3 billion in additional bank deposits, 85% of our current total deposits are FDIC insured." - Ron Kruszewski Chairman and CEO Press Release February 23, 2023 Stifel Reports January 2023 Operating Data "In January, we continued to benefit from our client cash initiatives. Client deposits remained relatively stable and we've continued to see similar trends so far in February. Total client assets increased 5% as we again benefitted from market appreciation and solid advisor recruiting. The environment for our Institutional Group remains challenging. However, the performance of both our transactional and investment banking businesses in January were in line with the outlook we gave on our fourth quarter conference call." - Ron Kruszewski Chairman and CEO Press Release February 16, 2023 Stifel to Meet With Institutional Investors at the KBW Winter Financial Services Symposium February 15-17, 2023 Presentation January 25, 2023 Stifel Reports Fourth Quarter 2022 Results "Stifel recorded its second best annual results in 2022. Our balanced business mix, which included record results in our Global Wealth Management segment and our third best year in our Institutional Group, enabled us to deliver a return on tangible common equity of 22%. Simply stated, Stifel performed as we expected. The breadth of our franchise helped to offset much of the impact of the difficult market environment in 2022. Given our continued strong financial performance and optimistic long-term outlook, I’m pleased to announce that our board has approved a 20% increase to our common dividend. This is our fifth consecutive annual dividend increase." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides 2022 December 22, 2022 Stifel Reports November 2022 Operating Data "Our Wealth Management business continued its strong performance in November. Client cash balances increased 2.1% and continued to increase month-to-date in December, driven by net inflows. Additionally, improved equity market performance and solid recruiting of financial advisors resulted in growth in total client assets and fee-based client assets of 4.5% and 4.7%, respectively. Our Institutional Group is on track for a strong year despite continued headwinds from the market environment. Our M&A pipelines remain strong, but delays in transaction closings will result in lower than anticipated Advisory revenue in the quarter and lower activity levels in our rates business will negatively impact our fixed income transactional business." - Ron Kruszewski Chairman and CEO Press Release December 07, 2022 Stifel to Meet With Institutional Investors at the Goldman Sachs U.S. Financial Services Conference Presentation November 28, 2022 Stifel Reports October 2022 Operating Data "Client deposits increased over the prior month as we continue to retain our existing deposit base and bring in new cash balances. I would note that our client cash balances have continued to increase so far in November. Client assets and fee-based assets increased 5% as a function of both financial advisor recruiting and appreciation in equity markets. The environment remains challenging for our Institutional Group and quarterly revenues will be driven by market conditions and the timing of advisory transactions." - Ron Kruszewski Chairman and CEO Press Release October 26, 2022 Stifel Reports Third Quarter 2022 Results "Stifel posted strong results, led by our Global Wealth Management segment, which generated its seventh consecutive record quarter. For the first nine months of the year, we are on track to record our second-strongest annual revenue and earnings per share. Our diversified business model continues to generate strong returns as our year-to-date return on tangible common equity is more than 21% and we are well positioned to not only return excess capital to shareholders, but to fund further growth in our business." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides August 09, 2022 Stifel to Meet With Institutional Investors at the UBS Financial Services Conference Presentation July 27, 2022 Stifel Reports Second Quarter 2022 Results "The diversity of our business resulted in another strong start to the year, as our first half net revenue and EPS are the second highest in the firm’s history. Market conditions are volatile and difficult to predict, and, as such, we will remain both cautious and opportunistic. Stifel is well positioned for continued growth as our capital levels remain robust and, as we have done throughout our history, we will use periods of market dislocation to reinvest in our business for future growth." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides May 24, 2022 Stifel to Meet With Institutional Investors at the UBS Wealth & Asset Management Conference Presentation April 27, 2022 Stifel Reports First Quarter 2022 Results "Our quarterly revenue and earnings per share were the second highest first quarter results in the firm's history. The strength of Stifel's business model continues to be its diversity as record Global Wealth Management and Institutional Fixed Income transactional revenue helped to offset the headwinds in some of our businesses. The outlook for the remainder of 2022 is strong. We expect to benefit from our significant asset sensitivity and the continued growth in other business lines" - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides April 25, 2022 Stifel Financial Releases Inaugural Environmental, Social & Governance Report Stifel Financial Corp. (NYSE: SF) will release its first quarter 2022 financial results before the market opens on Wednesday, April 27, 2022. The company will host a conference call to review the results at 9:30 a.m. Eastern time that same day. The conference call may include forward-looking statements. All interested parties are invited to listen to Stifel Chairman and CEO Ronald J. Kruszewski by dialing (877) 876-9938 and referencing conference ID 2672496. A live audio webcast of the call, as well as a presentation highlighting the company's results, will be available through Stifel's website, www.stifel.com . For those who cannot listen to the live broadcast, a replay of the broadcast will be available through the above-referenced website beginning approximately one hour following the completion of the call. Stifel 2021 ESG Report Stifel 2021 SASB Index Press Release February 17, 2022 Stifel to Meet With Institutional Investors at the KBW Winter Financial Services Symposium February 17-18, 2022 Presentation January 26, 2022 Stifel Reports Fourth Quarter and Full Year 2021 Results "2021 was an outstanding year for our firm as we delivered our 26th consecutive year of record net revenue, our fifth straight year of record earnings per share, and we generated a non-GAAP return on tangible common equity of 31% for the year. Each of our operating segments posted record results as the growth of our product offering has made us increasingly relevant to both wealth management and institutional clients. As I look forward, the outlook for Stifel remains as strong as I've seen in my 25 year tenure as CEO." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides 2021 November 03, 2021 Stifel to Meet With Institutional Investors at the Wolfe Research Wealth Symposium November 3, 2021 Presentation October 27, 2021 Stifel Reports Third Quarter 2021 Results "The third quarter's net revenue and EPS were the second highest in our history as both operating segments, Global Wealth Management and Institutional Group, generated strong results. We are on track to generate the 26th consecutive year of record revenue and, through three quarters, we have already surpassed our full year record EPS, set last year. Our success is driven by the continued reinvestment in the business. Based on the strength of our recruiting and investment banking pipelines, as well as our leverage to higher interest rates, Stifel remains well positioned to continue and build upon our decades long growth." - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides September 29, 2021 Stifel to Acquire Vining Sparks Enhances Stifel's fixed income depository coverage Press Release Slides July 28, 2021 Stifel Reports Second Quarter 2021 Results "Our results in the first half of 2021 are impressive. We generated consecutive quarters of record net revenue and our improved operating scale drove record non-GAAP earnings for both the quarter and the first half of the year. With both of the firm's operating segments driving our performance, I am optimistic that these results can be sustained, as illustrated by our increased guidance for the remainder of the year" - Ron Kruszewski Chairman and CEO Earnings Release Supplement Slides July 15, 2021 Stifel Announces Pricing of $300 Million of Non-Cumulative Perpetual Preferred Stock Press Release June 09, 2021 Stifel to Meet With Institutional Investors at the Stifel 2021 Cross Sector Insight Conference June 9-10, 2021 Presentation February 24, 2021 Stifel to Present at the Credit Suisse Financial Services Forum Press Release January 29, 2021 Stifel Reports Record Fourth Quarter and Full-Year Results "Stifel's performance in 2020 was a testament to the strength of our business model as we were able to navigate market challenges and generated our 25th consecutive year of record net revenue. We entered the year expecting two of our primary growth drivers to be continued strength in net interest income and advisory revenue. However, following the pandemic outbreak, Stifel and our associates were able to successfully pivot to the subsequent changes in the market environment and we finished the year with record results from our brokerage and capital raising businesses. Our record revenue also generated record non-GAAP earnings per share of $4.56 and non-GAAP return on tangible equity of nearly 25%." "We entered 2021 with record recruiting and investment banking pipelines, record client assets, and more than $500 million of excess capital. These factors, combined with our long history of profitable growth, drive my optimism for 2021 and beyond." - Ron Kruszewski Chairman and CEO Earnings Release Slides 2020 December 09, 2020 Stifel to Present at the Goldman Sachs U.S. Financial Services Conference Press Release Presentation November 12, 2020 Stifel to Present at the JMP Securities Financial Services & Real Estate Conference Press Release Presentation November 12, 2020 Stifel Announces a Three-for-Two Stock Split & Intention to Increase its Common Stock Dividend by 32% Represents Fourth Consecutive Annual Dividend Increase Delivered to Shareholders Press Release Frequently Asked Questions October 27, 2020 Stifel Reports Third Quarter 2020 Results "The financial performance during the quarter, and over the past few years, has been driven by a diverse business mix that's enabled both our Institutional Group and Wealth Management segments to generate strong growth. This diversification is illustrated by record nine month Wealth Management revenues despite significant declines in net interest income and deposit sweep fees, both a result of the Federal Reserve's implementation of a zero rate environment. Likewise, we achieved record nine-month Institutional Group revenues as record capital raising and brokerage revenues more than compensated for the 13% decline in advisory revenue." "Simply, Stifel is a growth company with diversified, balanced, and synergistic businesses. Over the last 12 months, Wealth Management, under both brokerage and fee-based models, has contributed 46% of net revenues. Institutional revenues, comprised of equity and fixed income, investment banking and trading, made up 41% of net revenues, while net interest income accounted for the remaining 13%. The complementary nature of these businesses is reflected in our return on tangible common equity, which is 23% over the past 12 months." - Ron Kruszewski Chairman and CEO Earnings Release Slides September 09, 2020 Third Quarter 2020 Investor Presentation Presentation July 29, 2020 Stifel Reports Second Quarter 2020 Results "The benefits of the investments we have made in our business were again evident in our second quarter results. Net revenue and earnings per share were the third highest in our history. Our Institutional Group had a record quarter led by record fixed income brokerage revenue as well as strong investment banking results primarily from robust capital raising activity in both fixed income and equity. This helped to counter the impact of the market sell off in the first quarter and the zero rate environment had on revenue lines such as asset management and net interest income, respectively. In terms of the future, I remain cautiously optimistic. We enter the third quarter, with a strong and liquid balance sheet, our strongest capital ratios in nearly four years, and a solid and improving recruiting pipeline. While the economic outlook for the second half of the year remains unclear, I believe that the performance of our diversified financial services model will remain strong." - Ron Kruszewski Chairman and CEO Earnings Release Slides April 30, 2020 Stifel Reports First Quarter 2020 Results "Given the impact of the current healthcare crisis on the economy and our daily lives, I'd like to say how proud I am of my Stifel partners and associates who have shown resolve, creativity, and teamwork to achieve the dual objectives of promoting the safety of our people while delivering essential and exceptional service to our clients. Our results in the first quarter illustrate the value of our diversified business as we generated our second highest quarterly revenue despite the sudden and dramatic change in the economy following the COVID-19 outbreak. Record Global Wealth Management revenue and our second strongest quarter for our Institutional Group was driven by record brokerage revenue as well as strong investment banking, net interest income and fee-based revenue. Given the operating conditions in the quarter, I believe that our brokerage results deserve special praise. In a matter of days, and with a focus on employee safety, we rapidly deployed our business continuity plan that resulted in more than 90% of our employees working remotely and enabled our Institutional Group to go from eight primary trading desks to more than 180 separate trading locations. This underscores the value of the investments we have made over the years in both people and technology." "The next few months have a high level of uncertainty, which can drive a wide range of economic outcomes. Longer term, we believe the world and our economy will overcome this pandemic. Looking forward, Stifel is well positioned because of its diversified business model, solid and liquid balance sheet, and our associates' commitment to excellence." - Ron Kruszewski Chairman and CEO Earnings Release Slides March 19, 2020 A message from Chairman and CEO Ron Kruszewski Presentation February 13, 2020 Stifel to Meet With Institutional Investors at the KBW Winter Financial Services Symposium Boca Raton Resort February 13-14, 2020 Presentation January 30, 2020 Stifel Reports Record Fourth Quarter and Full-Year Results "2019 was a remarkable year for Stifel as we achieved our 24th consecutive year of record net revenues. We continued to execute on our long-term strategy of combining organic growth with accretive acquisitions. As evidenced by our record non-GAAP earnings per share of $6.10, non-GAAP return on tangible equity of nearly 25%, and the return of more than $300 million to common shareholders through repurchases and dividends, we have been able to grow our business and enhance shareholder value." "As I look to 2020, I am optimistic about our success. The market environment remains strong and we enter the year with record client assets, robust recruiting and investment banking pipelines, and incremental revenue from the six acquisitions we closed in 2019. In short, Stifel remains well positioned to capitalize on current market trends as the diversity of our business model enables us to generate strong results in various operating environments." - Ron Kruszewski Chairman and CEO Earnings Release Slides 2019 November 13, 2019 Stifel to Meet With Institutional Investors at the JMP Securities Financial Services Conference JMP Securities Financial Services Conference Thursday, November 14, 2019 The St. Regis New York in New York City Presentation October 30, 2019 Stifel Reports Third Quarter 2019 Financial Results "I am pleased with our performance in the third quarter. We generated record net revenue of $822 million, a pre-tax margin greater than 20%, and return on tangible common equity of more than 24%. Our record results and the growth in the business validate our long-term strategy to build a diversified financial services firm that can consistently generate strong performance in various market conditions. This was illustrated by our record wealth management revenue and our second strongest institutional revenues in our history despite less than optimal market conditions. As I look forward, I am optimistic about our business as the investments we've made in our firm will continue to drive revenue growth." - Ron Kruszewski Chairman and CEO Earnings Release Slides September 09, 2019 Third Quarter 2019 Investor Presentation Presentation July 30, 2019 Stifel Reports Second Quarter 2019 Financial Results "I'm very pleased with our second quarter results. The $801 million in net revenue in the second quarter was the second highest quarterly revenue in our history and, through the first half of 2019, we have generated a record $1.6 billion of net revenue. In addition to our revenue growth, our expense discipline contributed to margins of nearly 20% in the quarter which drove non-GAAP return on tangible common equity of 23% and non-GAAP earnings per share of $1.41, up nearly 16% over 2018. We repurchased approximately 2.3 million shares in the first half of the year while maintaining our commitment to growing our business both organically and through accretive acquisitions. In the first half of 2019, we recruited 80 financial advisors and our pipeline remains very strong. Additionally, we announced two acquisitions in the first half of 2019 and closed another. As I look forward, I am optimistic about the second half of 2019 as well as our longer-term future." - Ron Kruszewski Chairman and CEO Earnings Release Slides May 15, 2019 Second Quarter 2019 Investor Presentation Presentation April 30, 2019 Stifel Reports First Quarter 2019 Financial Results "Stifel generated record first quarter results highlighting the diversity of our business model. Net revenue of $770 million increased 3% from last year's first quarter record and we achieved non-GAAP pre-tax margins of 19% and return on tangible equity of 22%. Our Global Wealth Management segment posted record net revenues and record pre-tax income, overcoming the expected decline in asset management revenue resulting from the market sell off in the fourth quarter of 2018. Our Institutional Group generated strong advisory, public finance, and fixed income brokerage revenue that helped to minimize the impact of the government shutdown on our equities business. As we look forward, we are optimistic about our business prospects for the remainder of 2019 and beyond." - Ron Kruszewski Chairman and CEO Earnings Release Slides April 18, 2019 Stifel to Acquire Mooreland Partners Press Release February 13, 2019 Stifel to Meet With Institutional Investors at the KBW Winter Financial Services Symposium Boca Raton Resort February 14-15, 2019 Presentation February 01, 2019 Stifel Reports Fourth Quarter & Full Year 2018 Financial Results "We had a great year. 2018 represented our 23rd consecutive year of record net revenues as our wealth management segment generated record results and our institutional business posted its second strongest year. Additionally, our focus on expense management contributed to our record pre-tax income and net income available to shareholders. Our non-GAAP return on common and tangible equity was 14.9% and 24.4%, respectively, and we returned approximately $215 million to shareholders through dividends and share repurchases." "As I look forward, I'm highly optimistic about our ability to grow and add value to our clients and our shareholders. We continue to expand our wealth management business through successful recruiting of financial advisors as well as through our bank. In our institutional business, our growth will continue to be driven by the addition of high quality talent through selective hires and strategic acquisitions. While market conditions can be volatile, our long term strategy remains focused on growth and deploying our capital with a focus on generating the best risk adjusted returns." - Ron Kruszewski Chairman and CEO Earnings Release Slides Home Investor Relations Annual Report and Proxy Statement Earnings Releases SEC Filings Press Releases Board of Directors Corporate Governance Sustainability Practices Money Market Tax Info SF Financial Strength IRS Form 8937 Market Risk Rule Disclosures --> Stifel is a diversified global wealth management and investment banking company focused on building relationships that help individuals, families, and organizations pursue their financial goals. The iconic image of the bull and bear shows the dedication and spirit of each member of our firm. We are individually and collectively committed to using the forces of the market to benefit our clients. Serving Clients How We Serve Clients Individuals & Families Institutions & Corporations Banking & Lending Cyber Security Financial Advisors Investors Research Asset Management Stifel Europe About Us About Stifel Get to Know Stifel Annual Report Connect With Stifel Register for Account Access Client Login Stifel Mobile Careers Stifel Careers Build Your Career Financial Advisors: Choose Stifel Campus Connection Benefits Map My Career Regulatory Links Important Disclosures Your Privacy FINRA BrokerCheck SEC Form ATS-N Use of Information Policy Bus | 2026-01-13T09:29:11 |
https://cgit.freebsd.org/ports/ | ports - FreeBSD ports tree index : ports 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 2024Q1 2024Q2 2024Q3 2024Q4 2025Q1 2025Q2 2025Q3 2025Q4 main FreeBSD ports tree about summary refs log tree commit diff log msg author committer range Branch Commit message Author Age main audio/exaile: update to 4.2.1 Ruslan Makhmatkhanov 12 min. 2025Q4 www/waterfox: Add ffmpeg8 support Martin Filla 12 hours 2025Q3 ports-mgmt/poudriere-devel: Update to 3.3.0-2209-g282bb7247 Bryan Drewery 3 months 2025Q2 security/sudo: security update to 1.9.17p1 FiLiS 6 months 2025Q1 math/hs-Agda: Disable building emacs mode files until upstream fixes it Gleb Popov 9 months 2024Q1 multimedia/obs-studio: Broken on armv7 Yuri Victorovich 11 months 2024Q4 games/mizuma: describe usability with KDE Plasma Alexander Vereeken 12 months 2024Q3 emulators/flexemu: Update to 3.22 Rainer Hurling 15 months 2024Q2 www/rt50: Update to 5.0.7 Mikael Urankar 18 months 2023Q4 emulators/yuzu: update to s20240102 Jan Beich 2 years [...] Tag Download Author Age release/15.0.0 commit 715bf41195... Colin Percival 8 weeks 14.2-eol commit 4e450f33b9... Rene Ladan 3 months 13.4-eol commit ba96410bb8... Rene Ladan 6 months release/14.3.0 commit a764c06788... Colin Percival 8 months 14.1-eol commit 71ffb8bcea... Rene Ladan 9 months release/13.5.0 commit 2cbed77221... Colin Percival 10 months 13.3-eol commit bd3f32dcf8... Rene Ladan 12 months release/14.2.0 commit 019c097f52... Colin Percival 14 months 14.0-eol commit d5d82c5ff9... Rene Ladan 15 months release/13.4.0 commit 8103237c5a... Colin Percival 17 months [...] Age Commit message Author Files Lines 12 min. audio/exaile: update to 4.2.1 HEAD main Ruslan Makhmatkhanov 3 -96 / +6 3 hours x11/quickshell: Add port: Building blocks for your desktop Hiroki Tagato 12 -0 / +321 7 hours */Makefile: Sort SUBDIRs Po-Chuan Hsieh 3 -3 / +3 8 hours math/Imath: default-enable PYTHON Matthias Andree 1 -1 / +2 9 hours net-mgmt/librenms: Update to 26.1.1 Dan Langille 2 -7 / +7 9 hours devel/gamin: Unbreak and improve port Daniel Engberg 1 -39 / +32 9 hours devel/fam: Remove port Daniel Engberg 25 -601 / +1 9 hours Mk/Uses/fam.mk: Remove helper Daniel Engberg 1 -60 / +0 9 hours treewide: Remove references to USES= fam and move to LIB_DEPENDS Daniel Engberg 14 -18 / +26 9 hours treewide: Remove library dependencies of devel/fam Daniel Engberg 1 -4 / +1 [...] Clone (public-mirror) https://git.FreeBSD.org/ports.git ssh://anongit@git.FreeBSD.org/ports.git (developers-only) ssh://git@gitrepo.FreeBSD.org/ports.git generated by cgit v1.2.3 ( git 2.25.1 ) at 2026-01-13 09:29:11 +0000 | 2026-01-13T09:29:11 |
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https://www.FreeBSD.org/community.html | Community | The FreeBSD Project Donate to FreeBSD Home About Introduction Features Privacy Policy Projects Ports Get FreeBSD Release Information Release Engineering Documentation FAQ Handbook Porter's Handbook Developer's Handbook Committer's Guide Manual Pages Presentations and Papers Documentation Project Primer All Books and Articles Community Mailing Lists Forums User Groups Events FreeBSD Journal Developers Project Ideas Git Repository GitHub Mirror Code Review (Phabricator) Wiki Continuous Integration Service Support Vendors Security Information Bug Reports Submitting Bug Reports Web Resources Foundation Monetary Donations Hardware Donations BSD Now Discord Events Forums IRC Mailing Lists Reddit User Groups YouTube Community FreeBSDâs community is diverse and extensive, and can be found everywhere great conversations and activity takes place. Youâll find our community and spaces intelligent, welcoming and approachable. The project maintains several official community spaces where developers, contributors and users can find help and support, stay up to date, coordinate and collaborate or just kick back, relax and socialise. If you prefer asynchronous and long-lived communications channels, FreeBSD offers over one hundred mailing lists covering a vast range of topics, and provides web-based forums and several Usenet newsgroups . For more real-time collaboration, we have a number of FreeBSD IRC channels where you can find support, learn to contribute, and a whole lot more. If you are interested in professional development and networking opportunities then follow us on LinkedIn and join our official FreeBSD LinkedIn Group . For regularly run in-person and online meets, you might like to look into our FreeBSD Events where we maintain a feed and calendar of upcoming events. Last year there were 7 events around the world. Beyond officially run community spaces, there are many User Groups , along with highly active FreeBSD communities on Reddit , Discord , StackOverflow , ServerFault , and MeetUp.com , among many other technology-related spaces. The FreeBSD wiki contains information about development and related projects. Video Content On YouTube, we host two official channels. On the main FreeBSD channel youâll find technical content, and on the FreeBSD Meetings channel youâll find live presentations, conferences, and working group meetings. Feel you donât belong here? FreeBSD is a system built piece by piece by volunteers from all walks of life. If you see something you think you can improve, we want to receive a patch, or a problem report, or a mail from you. There is room for everyone to help, and many of us have found enormous challenge and meaning in doing so. Last modified on : November 2, 2025 by Dag-Erling SmÞrgrav Legal Notices | © 1995-2026 The FreeBSD Project All rights reserved. The mark FreeBSD is a registered trademark of The FreeBSD Foundation and is used by The FreeBSD Project with the permission of The FreeBSD Foundation . Contact | 2026-01-13T09:29:11 |
https://www.FreeBSD.org/projects/index.html | FreeBSD Development Projects | The FreeBSD Project Donate to FreeBSD Home About Introduction Features Privacy Policy Projects Ports Get FreeBSD Release Information Release Engineering Documentation FAQ Handbook Porter's Handbook Developer's Handbook Committer's Guide Manual Pages Presentations and Papers Documentation Project Primer All Books and Articles Community Mailing Lists Forums User Groups Events FreeBSD Journal Developers Project Ideas Git Repository GitHub Mirror Code Review (Phabricator) Wiki Continuous Integration Service Support Vendors Security Information Bug Reports Submitting Bug Reports Web Resources Foundation Monetary Donations Hardware Donations Developer's Handbook Committer's Guide Porter's Handbook Release Engineering Platforms Project Ideas Contributing FreeBSD Development Projects In addition to the mainstream development path of FreeBSD, a number of developer groups are working on the cutting edge to expand FreeBSDâs range of applications in new directions. Follow the links below to learn more about these exciting projects. If you feel that a project is missing, please send the URL and a short description (3-10 lines) to www@FreeBSD.org . In addition, some of these projects regularly submit status reports, which can be viewed on the status reports page . Documentation Advocacy Applications Storage Kernel and Security Device drivers Architecture Misc Google Summer of Code Documentation FreeBSD Documentation Project : The FreeBSD Documentation Project is a group of people who maintain and write the documentation (such as the Handbook and FAQ) for the FreeBSD project. If you want to help with the documentation project, subscribe to the freebsd-doc@FreeBSD.org mailing list and participate. FreeBSD Resources for Newbies : A list of resources to help those new to FreeBSD and UNIX® in general. The FreeBSD Diary : A collection of how-to entries aimed at UNIX novices. The aim is to provide a set of step-by-step guides to installing and configuring various ports. The FreeBSD Developers' Handbook Contributing to FreeBSD Applications Java® on FreeBSD : This contains information on where to obtain the latest JDK® for FreeBSD, how to install and run it, and a list of Java® software that you may find interesting. GNOME on FreeBSD : This contains information on where to obtain the latest GNOME for FreeBSD, how to install and run it, latest project news and updates, FAQ covering FreeBSD-specific GNOME issues, application porting guidelines and much more. KDE on FreeBSD : This contains links to the latest KDE releases for FreeBSD, as well as documentation and tutorials about how to install and run KDE on FreeBSD. Project news and a FreeBSD-specific FAQ are also available. Mono on FreeBSD : Here you can find information about the state of Mono and C# for FreeBSD. OpenOffice.org on FreeBSD : Information about the various OpenOffice.org ports. FreeBSD Ports Collection : The FreeBSD Ports Collection provides an easy way to compile and install a wide range of applications with a minimum amount of effort. A list of current ports is available along with a search mechanism to see if a specific application exists in the Ports Collection. FreeBSD Ports distfiles scanner : A list which checks the Ports Collection for unfetchable distfiles and provides a summary for each port. FreshPorts : Provides the most up-to-date list of ports and port changes. Add your favourite ports to your watch list and receive email notification of any changes. Storage Coda : A distributed filesystem. Among its features are disconnected operation, good security model, server replication and persistent client side caching. Journaling versus Soft Updates : Asynchronous Meta-data Protection in File Systems. Kernel, security TrustedBSD : Provides a set of trusted operating system extensions to the FreeBSD operating system. This includes features such as fine-grained privileges (capabilities), Access Control Lists, and Mandatory Access Control. These features are being integrated back into the base FreeBSD distribution, as well as being ported to other BSD-derived systems. Kernel Stress Test Suite : The purpose of this stress test is to crash the system. The stress test is composed of small test programs and scripts. Each test targets a specific area of the kernel. The key concept of this test suite is chaos. Each test sleeps for a random number of seconds before it starts up in a random number of invocations. Device drivers Home Automation : Using FreeBSD to run appliance controllers, infra-red controllers, automated telephone systems, and more. Architecture Porting FreeBSD to PowerPC® systems : Contains information on the FreeBSD PPC port, such as mailing list information and so on. The OSKit : The OSKit is a framework and a set of 31 component libraries oriented to operating systems, together with extensive documentation. By providing in a modular way not only most of the infrastructure "grunge" needed by an OS, but also many higher-level components, the OSKitâs goal is to lower the barrier to entry to OS R&D and to lower its costs. The OSKit makes it vastly easier to create a new OS, port an existing OS to the x86 (or in the future, to other architectures supported by the OSkit), or enhance an OS to support a wider range of devices, filesystem formats, executable formats, or network services. The OSKit also works well for constructing OS-related programs, such as boot loaders or OS-level servers atop a microkernel. Misc NanoBSD : NanoBSD is a tool designed to create a possibly reduced FreeBSD system image, which is suited to fit on a Compact Flash card (or other mass storage medium) in a way which is suitable for use in appliance like applications. The FreeBSD documentation collection includes an introductory article about NanoBSD , which includes useful tips about setting up, running and using NanoBSD. GLOBAL : A common source code tag system that works the same way across diverse environments. Currently, it supports the shell command line, the nvi editor, web browser, the emacs editor, and the elvis editor, and the supported languages are C, Yacc, and Java. ACPI on FreeBSD : A Project created to get ACPI working smoothly on FreeBSD. TestSuite : This project aims to equip FreeBSD with a comprehensive test suite that is easy to run out of the box and during the development of the system. The goal of the test suite is to assist both developers and users in assessing the quality of FreeBSD. Last modified on : April 21, 2022 by Danilo G. Baio Legal Notices | © 1995-2026 The FreeBSD Project All rights reserved. The mark FreeBSD is a registered trademark of The FreeBSD Foundation and is used by The FreeBSD Project with the permission of The FreeBSD Foundation . Contact | 2026-01-13T09:29:11 |
https://www.FreeBSD.org/usergroups.html | User Groups | The FreeBSD Project Donate to FreeBSD Home About Introduction Features Privacy Policy Projects Ports Get FreeBSD Release Information Release Engineering Documentation FAQ Handbook Porter's Handbook Developer's Handbook Committer's Guide Manual Pages Presentations and Papers Documentation Project Primer All Books and Articles Community Mailing Lists Forums User Groups Events FreeBSD Journal Developers Project Ideas Git Repository GitHub Mirror Code Review (Phabricator) Wiki Continuous Integration Service Support Vendors Security Information Bug Reports Submitting Bug Reports Web Resources Foundation Monetary Donations Hardware Donations BSD Now Discord Events Forums IRC Mailing Lists Reddit User Groups YouTube User Groups FreeBSDâs widespread popularity has spawned a number of user groups around the world. If you know of a FreeBSD user group not listed here, please fill out a problem report in category DocumentationâWebsite with the following information: A URL for the user groupâs website. An email contact address of a human in charge, for use by our visitors and website administrators. A short (one paragraph) description of the user group. In keeping with the spirit of FreeBSD, we prefer user groups that are active and which conduct their business in public. If there is no local group, http://bsd.meetup.com/ can be used to locate interested individuals near by. Consider forming your own user group! Regions FreeBSD User Groups can be found worldwide, connecting enthusiasts and professionals who share a passion for this versatile operating system. Join a FreeBSD User Group in your region and become part of this vibrant community, networking and learning from fellow FreeBSD enthusiasts near you. Europe Wiener BSD Stammtisch The Vienna BSD-Stammtisch is an English and German speaking BSD user group located in Austriaâs capital. We meet once a month to have a show and tell session, discuss and have a beer or two. The idea is to improve our skills and meet other BSD enthusiasts. Feel free to join! Prague BSD Group A small BSD user group from Brmlab Prague/Czech Republic Hackerspace. We are a group of hobbyists, professionals, and enthusiasts. BSD-DK The Danish BSD User Group. Promotion and support of the BSD derived Operating Systems in Denmark. Mailing lists, lecture and workshops. Send mail subscription requests to bsd-dk-request@bsd-dk.dk . Located in Denmark. AlbaBUG The Albanian BSD Users Group is a group that represents and supports *BSD in Albanian language. More information is available at https://www.AlbaBSD.org . Located in Prishtina, Kosova. Augusta The BSD User Group of the Augsburger Computer Forum e.V. meets every second Saturday at 17.00 Please visit our web site for more information on dates and our address. We have all kinds of BSD, but mainly FreeBSD and Mac OS X. Located in Germany, Augsburg. BSD User Group Hamburg (BSDHH) The BSD User Group Hamburg (BSDHH) meets on the first Wednesday of the month at 7.00pm in the restaurant Léon (Koppel 1, 20099 Hamburg). Most members are FreeBSD users, although users of all BSD flavors are welcome. Located in Germany, Hamburg. BSD User Group DÃŒsseldorf/NRW (BSD.NRW) BSD.NRW is located in DÃŒsseldorf, NRW, Germany. We are open for all people in the Rhein-Ruhr area that are interested in the BSD family. We have bi-monthly meetings (always on the second Tuesday of these months). Upcoming dates and additional information can be found on the BSD.NRW website . HappaBSD Regensburg HappaBSD is a regulars' table taking place every month in Regensburg, Bavaria, Germany. For more information see the HappaBSD website . Unix Users Group Rhein-Neckar e.V. (UUGRN e.V.) The Unix Users Group Rhein-Neckar e.V. (UUGRN e.V.) provides a regional forum for users of all Unix flavors, with a stress on Linux and BSD. Meetings (UnixUserStammtisch) are held on the third Monday of each month. The Hands-on FIXME meeting happens every first Friday of each month. Located in Germany, Rhine-Neckar-Region , Heidelberg. All events get announced on X @uugrn . Berkeley in Munich (BIM) The Berkeley in Munich (BIM) caters for users of BSD based systems in Oberbayern. Located in Germany, Munich. Ulmer BSD User Stammtisch The U-BUS meets every last Thursday of the month. For more information about meetings and related issues, please check the web page ( u-bus ). Located in Germany, Ulm. Bluefrogs e.V. Unix and Linux User Group in Bergisch Gladbach (near Cologne), German. Meetings are held twice a month. We are working with all Open Source Unix derivates: FreeBSD, NetBSD, OpenBSD, Linux and others. We give workshops, lectures, talks and install parties. For more information please email us at: info@bluefrogs.de . BSE MÃŒnchen BSE (BSD Social Event) is located in MÃŒnchen (Munich), Germany. Feel free to visit our regular meetings (every four weeks) or join the mailing list. For more information visit the BSE web site . FUG-FR The FreeBSD User Group France (FUG-FR) is an group of FreeBSD enthusiasts in France currently involved translating marketing papers etc. They are also active in helping out FOSDEM (held in Brussels). For more information please visit our web page http://www.fug-fr.org or contact Rodrigo Osorio . Dublin BSD User Group The Dublin BSD User Group is a collective of individuals who are interested in the application of BSDs in production scenarios. We are interested in how the BSDs can help both sysadmins and application developers deploy and manage robust solutions as well as looking at some of the unique features of the BSDs. For more information please visit our website . Lissyara.su We are a russian FreeBSD community. On our site we offer several articles for setting up FreeBSD and various other applications. In addtition to the articles we also provide a user forum where experienced users of FreeBSD are pleased to be able to help other users. Russian FreeBSD User Group (RFUG) The Russian FreeBSD User Group (RFUG) is a Russian language oriented user group for the Russian and ex-USSR users. Located in Russia. Ulyanovsk FreeBSD Users Group The Ulyanovsk FreeBSD Users Group (ULBSD) is for Russian users of ULBSD operating system that is based on FreeBSD. We provide free and paid versions of our operating system. We also answer many questions of users for free and help them work in the FreeBSD and ULBSD. For more information send an email to info@ulbsd.ru or visit the website at http://www.ulbsd.ru/ . Located in Ulyanovsk, Russia. Gruppo Utenti FreeBSD Italia (GUFI) The Gruppo Utenti FreeBSD Italia (GUFI) is a "italian powered" FreeBSD User Group. It is intended to help Italian FreeBSD users to find support and articles on/about FreeBSD in the italian language. Please follow this link to know more about us. Located in Italia. BSD User Group Latvia To learn more about the BSD User Group Latvia, please visit our site and our forums . Located in Latvia. The Norwegian BSD User Group (NOBUG) The Norwegian BSD User Group (NOBUG) is a User Group for BSD users and enthusiasts in Norway. Meetings are currently held in Oslo and Bergen. Visit our website for more information. There is also a Unix User Group ( NUUG ) with more regular meetings, sometimes even with a BSD subject. Located in Norway. Polish BSD User Group Our group was created to promote systems from the BSD family in Poland. We organize meetings that bring together lovers of Unix systems. Located in Warsaw. SBUG SBUG is a *BSD user group based in Rzeszow and targeting the Podkarpackie Voivodeship. The Portuguese *BSD Users Group The Portuguese *BSD Users Group is a User Group for Portuguese users of BSD operating systems. Contact Rui Pereira ptbsd@yahoo.com for more information. Located in Portugal, Amadora. BSD User Group Slovenia BSD User Group Slovenia connects BSD system users in Slovenia. Group members also gather on the ##bsd.si IRC channel on Libera Chat. Located in Ljubljana, Slovenia. The Hungarian BSD Group The Hungarian BSD Group is a group of NetBSD, FreeBSD, and OpenBSD users located in Hungary. Their meetings occur on the second Friday of the month. The website of the group is http://www.bsd.hu/ . BSD Users Sweden (BUS) The BSD Users Sweden (BUS) maintains a mailing list. To join send an email to majordomo@stacken.kth.se with subscribe bus in the body. Located in Sweden. Manchester BSD Users Group The Manchester BSD Users Group meets reasonably often in the Lass OâGowrie, on Charles Street, Manchester. Contact Sam Smith for more information. Located in The United Kingdom, Manchester. FreeBSD UK Users group (FreeBSD UKUG) The FreeBSD UKUG (FreeBSD UK Userâs Group) exists for the benefit of FreeBSD users in the United Kingdom. Please follow the link for more information. Located in the United Kingdom. The BSD in Belfast Group Meet the BSD Belfast Group the third Friday of the month. We are a group of academic and industrial BSD users and admirers. FreeBSD TÃŒrkiye We are a community of FreeBSD enthusiasts and professionals dedicated to supporting and advancing FreeBSD systems. Our group actively engages in writing documentation, contributing code, and conducting research to enhance the FreeBSD ecosystem. Whether youâre an experienced developer or just starting with FreeBSD, join us in fostering collaboration and innovation within the Turkey FreeBSD community. Open Source Society Malta The Open Source Society Malta is a local community for the Open-Source Initiative in Malta supporting open-source enthusiasts and professionals. North America BSD User Group Oak Ridge (BUGOR) BUGOR is an open and free network of BSD users in the Oak Ridge TN area. Ideally, professional sysadmins, programmers and grizzled old hackers will meet each week to explore and debate the intricacies of the Unix design philosophy and the art of Unix programming while mentoring neophyte users and rehabilitating WIMP users. The Berkeley Unix User Group The Berkeley Unix User Group is a general Unix Users Group for anyone in the San Fransico Bay Area. We meet on a weekly basis in downtown Berkeley. Visit the web site or send a message to buug-request@weak.org with subscribe in the body. Located in Berkeley, CA. The Capitals District *BSD Users Group The Capitals District *BSD Users Group draws itâs membership from New Yorkâs capital district and the surrounding communities. CDBUG was founded in December of 2004 and has a small but growing membership. We currently meet on the last Sunday of every month in the Albany area. Our mailing list is graciously being hosted by NYCBUG at: http://lists.nycbug.org/mailman/listinfo/cdbug-talk . Located in Albany, NY. Baltimore Area BSD Operating System User Group (CharmBUG) CharmBUG is a Baltimore Area BSD Operating System User Group, where the focus is on FreeBSD, OpenBSD, NetBSD and DragonFly BSD, including system administration and security across all UNIX like operating systems. Please contact shirkdog@gmail.com for details. The Users of Free Operating Systems (UFO Chicago) The Users of Free Operating Systems (UFO Chicago) is an open-source Unix User Group that meets twice a month in Chicago. For directions and mailing list information, please check our web site http://ufo.chicago.il.us . Chicago, IL. The Colorado BSD Users Group (CoBUG) Created in 2014, CoBUG is a completely open BSD community, established with the intention of spreading love for the BSDs. Located in Colorado. HamBUG The Hamilton (Ontario, Canada) BSD User Group meets on the second Tuesday of each month. Meetings consist of open discussions, short demonstrations, and question & answer time. Users from the entire GTA (Greater Toronto Area), London, and Waterloo regularly attend. KnoxBUG The Knoxville, Tennessee BSD User Group (KnoxBUG) meets once a month. For more information, please visit http://knoxbug.org and/or subscribe to our mailing list . NMLUG The NMLUG in Alburquerque meets once a month and supports both BSD and Linux users. To join the mailing list, send a message to majordomo@swcp.com with subscribe nmlug in the body. Located in New Mexico. New York City *BSD User Group (NYCBUG) The New York City *BSD User Group (NYCBUG) meets the first Wednesday of the month. Mailing lists are available at http://lists.nycbug.org . Located in New York, NY. Yavapai Free Unix Users Group The Yavapai Free Unix Users Group is now forming for *BSD/Linux, etc., users in Northern Arizona. Please contact Russel Carter ( rcarter@consys.com ) for details. Located in Northern Arizona. Western Pennsylvania Linux Users Group (WPLUG) The Western Pennsylvania Linux Users Group (WPLUG) has a strong and growing community of BSD users that it supports. See our home page ( http://www.wplug.org ) for information on regular meetings and join the mailing lists. Located in Pennsylvania. Portland (Oregon) FreeBSD Users Group The Portland (Oregon) FreeBSD Users Group meets on the third Thursday of each month. Mail The Portland FreeBSD Users Group . Located in Portland, OR. Reno Linux Users Group (RLUG) The Reno Linux Users Group (RLUG) meets monthly in Reno, Nevada and discusses the use of BSD and Linux. Visit our website for more information, where you may also join our mailing list. Located in Reno, NV. Seattle BSD Users Group (SeaBUG) The Seattle BSD Users Group (SeaBUG) meets occasionally. View our web site for more details and for information on how to join our mailing list. Located in Seattle, WA. San Diego BSD Users Group (SDBUG) The San Diego BSD Users Group meets on the first Thursday of every month, usually at AleSmith on Miramar Rd. Check the website and mailing lists for specifics. Located in San Diego, CA. Tucson Free Unix Group (TFUG) Tucson Free Unix Group, Arizona. Located in Tucson, AZ. The Southern Utah Unix Users Group (SU3G) The Southern Utah Unix Users Group (SU 3 G, SU^3G, SU3G, or SUUUG) is for users of any version of Unix or Unix-like operating system, commercial or open source. So, whether you use FreeBSD, Linux, Mac OS X, BSD/OS, Solaris, OpenBSD, or any other Unix or Unix-like OS, and if you live, work, or associate with someone who lives or works in southwestern Utah, then consider joining the SU 3 G. For more information send an email to kendall@su3g.org or visit the website at http://www.su3g.org/ . Capital Area BSD Users Group We meet on the last Tuesday of the month. Join our mailing list or visit our web site here . We meet in Maryland, but welcome anyone from MD, DC or VA. Kansas City Area *BSD User.s Group We are a beginning BSD User Group with currently two members. If you would like to join, please visit our web site here . Our goals are to have fun, help others to learn about and using *BSD and evangelize the BSD operating systems. GTABUG The GTABUG User Group welcomes all BSD users. Monthly meetings give attendees a chance to share ideas, discussion and information. Installations and other events help preach the good news of BSD to the community. Come drop by for a meeting! Located in Greater Toronto Area, Ontario. Ottawa-Carleton Unix Users Group We are a small group of hobbyists, users, authors, sysadmins, and hackers that meet monthly to discuss the latest topics in a very informal setting. Check our mailing list for the latest information. Located in Ottawa, Ontario. The Orlando BSD Users Group A small BSD user group from the greater Orlando area in central Florida. We are a group of computer hobbyists, I/T professionals, and Makerspace enthusiasts. South America BSD Chile The BSD Chile User Group (bsd.cl) maintains mirrors, documents, and mailing lists for the Chilean BSD community. The group also provides infrastructure for BSD events in Chile and can be reached on the IRC server irc.bsd.cl #BSD in addition to the web site . The Brazilian FreeBSD User Group (FUG-BR) The Brazilian FreeBSD User Group (FUG-BR) is a Portuguese language oriented User Group intended to help Brazilian FreeBSD users to find support and articles on and about FreeBSD in the Portuguese language. We keep some projects such as the FreeBSD LiveCD . Currently the group has over 800 members and our mailing list has an average traffic of 80 messages per day. To join FUG-BR mailing list, visit https://www.fug.com.br/mailman/listinfo/www Asia The Kansai *BSD Users Group (K*BUG) The Kansai *BSD Users Group was established on November 13, 1999. It is expected to promote communication of any of the BSD variants' users. Some of its activities are to hold friendly parties of the members, and to hold seminars covering wide variety of topics. Please e-mail here ( kbug-admin@kbug.gr.jp ). Located in Kansai, Japan. The Echigo BSD Users Group (EBUG) The Echigo BSD Users Group is the users group for BSD users around Echigo (aka Niigata). For more information on our events and mailing lists, please check the EBUG web site. The Jogja FreeBSD Users Group The Jogja FreeBSD Users Group is based in Yogyakarta City, Indonesia. Send email to 22961476@students.ukdw.ac.id for more information. meetBSD Iran meetbsd.ir is an Iranian BSD user group that provides conferences, iso, ebooks, and PKGs for BSD local users. The MyBSD Malaysia Project The MyBSD Malaysia Project is a Kuala Lumpur based User Group for BSD users and open-source in general, promoting and supporting FreeBSD, OpenBSD, NetBSD and open source usage. We meet once a month, usually at Universiti Malaya or Restaurant Bahadur Shah. One of our projects is to develop a Unix file manager . Visit our web site or contact info@MyBSD.org.my for more information. New Delhi BSD User Group The New Delhi BSD User Group (NDBUG) is a group of hobbyists, professionals, and enthusiasts. Our members share interests in BSD Unix and free implementations of Unix, as well as other open source software. NDBUG was founded to disseminate knowledge of BSD Unix, open source software, and related technologies and interests in India and New Delhi specifically. BSD Taiwan BSD Taiwan is a BSD user group for Taiwanese BSD users, where we can share knowledge, ideas, and seek help. For more information please visit our website . Armenia BSD User Group Weâre a community of BSD Hackers, Unix Sysadmins and Unix-like system users who gather once a month (online and offline) to discuss BSD Unix related topics. Saudi Arabia BSD User Group Arbian open source community in Saudi Arabia, *BSD one of part of the community, represents and supports Arabic language. Oceania Home Unix Users Group for Brisbane We meet fortnightly at meeting rooms provided by the University of Queensland . More information, including how to join the mailing lists, is available at http://www.humbug.org.au . Located in Queensland. BUGA, The BSD User Group of Adelaide We meet at irregular intervals. Join the mailing list by sending a message to majordomo@lemis.com with the text subscribe BUGA in the body. Located in South Australia. BSD Users Group, Sydney (BUGS) The BSD Users Group, Sydney (BUGS) meets occasionally in Sydney and surrounds. We have members from all over NSW. To join the mailing list or IRC channel, visit http://www.bugs.au.freebsd.org/ . Located in New South Wales. Global The Global BSD User Groups The Global BSD User Groups organization was founded to help promote local BSD user groups by helping to share ideas and experiences with other BUG organizations. Our goals are: We are working on ways to provide remote participation through streaming video of presentations. You can check our website or contact us via e-mail . Last modified on : September 24, 2025 by Alexander Ziaee Legal Notices | © 1995-2026 The FreeBSD Project All rights reserved. The mark FreeBSD is a registered trademark of The FreeBSD Foundation and is used by The FreeBSD Project with the permission of The FreeBSD Foundation . Contact | 2026-01-13T09:29:11 |
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https://aws.amazon.com/blogs/big-data/cross-account-data-collaboration-with-amazon-datazone-and-aws-analytical-tools/ | Cross-account data collaboration with Amazon DataZone and AWS analytical tools | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Cross-account data collaboration with Amazon DataZone and AWS analytical tools by Arun Pradeep Selvaraj , Piyush Mattoo , and Mani Yamaraja on 05 MAR 2025 in Amazon DataZone , Amazon Redshift , AWS Glue , Technical How-to , Thought Leadership Permalink Comments Share Data sharing has become a crucial aspect of driving innovation, contributing to growth, and fostering collaboration across industries. According to this Gartner study , organizations promoting data sharing outperform their peers on most business value metrics. A straightforward data access and sharing mechanism is crucial for enabling effective data sharing across an organization. There are challenges such as complexity in managing cross-account permissions and difficulty in discovering the right data across accounts that organizations face when trying to share data products across AWS accounts. Amazon DataZone is a fully managed data management service that customers can use to catalog, discover, share, and govern data stored across Amazon Web Services (AWS) . In this post, we will cover how you can use Amazon DataZone to facilitate data collaboration between AWS accounts. Solution overview This solution provides a streamlined way to enable cross-account data collaboration using Amazon DataZone domain association while maintaining security and governance. This post describes the process of using the business data catalog resource of Amazon DataZone to publish data assets so theyâre discoverable by other accounts. After theyâve been published, you can query the published assets from another AWS account using analytical tools such as Amazon Athena and the Amazon Redshift query editor , as shown in the following figure. In this solution (as shown in the preceding figure), the AWS account that contains the data assets is referred to as the producer account . The AWS account that needs to access or use the data from the producer account is referred to as the consumer account . The Amazon DataZone domain is created and managed within the producer account and then the consumer account is associated with that domain. As part of Amazon DataZone domain association, Amazon DataZone uses AWS Resource Access Manager (AWS RAM) to share the resource. When the producer and consumer AWS accounts are in the same organization within AWS Organizations , the domain association happens automatically. If the producer and consumer AWS accounts are in different organizations, AWS RAM sends an invitation to the consumer AWS account to accept or reject the resource grant. This solution presents three Amazon DataZone user personas as: Data administrators : Account owners in both producer and consumer AWS accounts. The data administrators are responsible for creating Amazon DataZone domains, configuring domain associations, and accepting domain associations within the Amazon DataZone domain. Data publishers : Users in producer AWS accounts. The data publishers are responsible for creating Amazon DataZone publish projects and environments, producing and publishing data assets, and accepting subscription requests. Data subscribers : Users in consumer AWS accounts. The data subscribers are responsible for creating Amazon DataZone subscribe projects and environments, searching for and subscribing to data assets, and querying the data and deriving insights. Prerequisites To follow along with the instructions, you will need: Two AWS accounts, one serving as producer and other account serving as consumer. Create new AWS accounts if necessary. An Amazon Redshift provisioned cluster or Amazon Redshift Serverless workgroup in the producer and consumer AWS accounts provisioned by a data administrator. A secret in AWS Secrets Manager storing the master user credentials for the Amazon Redshift cluster or workgroup in the producer and consumer AWS accounts. The data administrators are responsible for creating secrets. The data producers and consumers can obtain the Amazon Resource Name (ARN) of the secrets from the data administrators during the environment or environment profile creation steps. Amazon DataZone uses Amazon Redshift Datashares to share data across clusters and accounts. There are specific requirements and limitations for using Amazon Redshift datashares. For cross-account data sharing, both the producer and consumer clusters must be encrypted. See Cluster encryption section of datashare-considerations for more information about the encryption process. Data sharing is supported only for provisioned ra3 cluster types (ra3.16xlarge, ra3.4xlarge, and ra3.xlplus) and Amazon Redshift Serverless. Walkthrough: The following are the high level steps to configure cross-account access. Weâve provided step-by-step instructions in the following sections. Create an Amazon DataZone domain in the producer account. The data administrator creates an Amazon DataZone domain. Request Amazon DataZone domain association from the producer account to the consumer account. Accept the domain association request in the consumer account. The data administrator accepts the domain association. Add data users to the Amazon DataZone domain. Create the necessary publish project for AWS Glue and Amazon Redshift in the producer account. Create AWS Glue and Amazon Redshift environments to publish the data assets in the producer account. Create and run a data source for AWS Glue and Amazon Redshift to publish assets into the business catalog. Create subscribe projects for AWS Glue and Amazon Redshift. Create AWS Glue and Amazon Redshift environment profiles and environments in the subscribe project Subscribe to AWS Glue and Amazon Redshift tables. Consume the data using Athena and Amazon redshift editors. This step is performed by the data subscriber. Create the Amazon DataZone domain in the producer account Amazon DataZone domains serve as high-level organizational units for assets, users, and projects, facilitating cross-team and cross-account collaboration. This step focusses on creating the Amazon DataZone domain in the producer account. Sign in to the producer account AWS Management Console for Amazon DataZone using the data administrator credentials. Create an Amazon DataZone domain titled Demo_cross_account_domain using the instructions at create domains . On the Create domain screen, select Quick setup checkbox to automate several configuration steps, saving time and reducing the potential for setup errors. Quick setup enables two default blueprints and creates the default environment profiles for the data lake and data warehouse default blueprints. Request Amazon DataZone domain association from the producer account to the consumer account To associate the Amazon DataZone domain with the consumer account, the producer account requests a domain association . This involves providing necessary information about the consumer account and granting appropriate permissions for data access and management. Sign in to the Amazon DataZone console of the producer account using the data administrator credentials. Navigate to the domain detail page, and then scroll down and select the Associated Accounts tab. Enter the consumer account IDs that you want to request association. Choose Add another account if you want to add more than one account. When youâre satisfied with the list of account IDs, choose Request association . Use the latest (AWS RAM DataZonePortalReadWrite policy when requesting the account association. This policy allows users in the consumer account to execute Amazon DataZone APIs and to use the data portal interface. Accept an account association request from an Amazon DataZone domain This step focuses on accepting the account association request from the Amazon DataZone domain in the consumer account. This allows the consumer account to be linked with the Amazon DataZone domain to enable data sharing and collaboration between the producer and consumer accounts. Sign in to the consumer account and go to the Amazon DataZone console in the same AWS Region as the domain. On the Amazon DataZone home page, choose View requests . Select the name of the inviting Amazon DataZone domain and choose Review request . Choose Accept association , you should see the Demo_cross_account_domain state as associated in the Associated domains screen Choose the domain for which you want to enable an environment blueprint. From the Blueprints list, choose either the DefaultDataLake blueprint On the Permissions and resources page, for enabling the DefaultDataLake blueprint, for Glue Manage Access role , specify a new role that grants Amazon DataZone authorization to ingest and manage access to tables in AWS Glue and AWS Lake Formation. Repeat steps 4 to 6 to enable the DefaultDataWarehouse blueprint by choosing DefaultDataWarehouse instead of DefaultDataLake Add data users to the Amazon DataZone domain To grant access to the Amazon DataZone data portal from the console for data publisher and data Subscriber IAM users, use the following steps to add them in the User Management section of the Amazon DataZone domain. See Manage users in the Amazon DataZone console for additional details. Sign in to the Amazon DataZone console as a data administrator using the producer account. Select the Amazon DataZone domain and, in the User management section, choose Add and select Add IAM users . On the Add users page, choose Current account and add the user ARN of the data producer and choose Add users . Next choose Associated account , and enter the data subscriber userâs ARN and add the user by choosing Add users . Create the publish project for AWS Glue and Amazon Redshift This step focuses on creating the publish project for AWS Glue and Amazon Redshift in the producer account. The project will be used to publish data from your data sources to the appropriate AWS services. Using the producer account, sign in to the Amazon DataZone console as a data publisher. Select View domains and select the demo_cross_account_domain . Choose the Open data portal link and sign in to the data portal. Choose Create New Project and create a project named Glue_Publish_Project for publishing AWS Glue data assets and create the project under demo_cross_account_domain . Create another project named Redshift_Publish_Project for publishing Amazon Redshift data assets, also under the demo_cross_account_domain . Create AWS Glue and Amazon Redshift environments to publish the data assets In this step, you set up AWS Glue and Amazon Redshift environments in the producer account to share data assets. The required infrastructure, such as the AWS Glue Data Catalog and Redshift cluster for storing data, should already be in place. After setup, this will allow the consumer account to access and use the shared data assets. See Create a new environment for detailed instructions on creating a new environment. Create the AWS Glue environment and a new AWS Glue table In the same Amazon DataZone domain demo_cross_account_domain, choose Browse Project and select the Glue_Publish_Project and create Glue_Publish_Environment using the default DataLakeProfile . Leave the producer_glue_db_name , consumer_glue_db_name and Workgroup_name blank. Choose Create Environment and wait for the process to complete. After the environment is created, browse the list of available projects and choose Glue_publish_project . Next, navigate to the Glue_Publish_Environment , and under Analytics tools , choose Amazon Athena to open the Athena query editor Choose Open Athena and make sure that Glue_Publish_Environment is selected in the Amazon DataZone environment dropdown at the upper right and that in Data on the left, glue_publish_environment_pub_db is selected as the Database . Create a new AWS Glue table for publishing to Amazon DataZone. Paste the following create table as select (CTAS) query script in the Query window and run it to create a new table named mkt_sls_table . The script creates a table with sample marketing and sales data. CREATE TABLE mkt_sls_table AS SELECT 146776932 AS ord_num, 23 AS sales_qty_sld, 23.4 AS wholesale_cost, 45.0 as lst_pr, 43.0 as sell_pr, 2.0 as disnt, 12 as ship_mode,13 as warehouse_id, 23 as item_id, 34 as ctlg_page, 232 as ship_cust_id, 4556 as bill_cust_id UNION ALL SELECT 46776931, 24, 24.4, 46, 44, 1, 14, 15, 24, 35, 222, 4551 UNION ALL SELECT 46777394, 42, 43.4, 60, 50, 10, 30, 20, 27, 43, 241, 4565 UNION ALL SELECT 46777831, 33, 40.4, 51, 46, 15, 16, 26, 33, 40, 234, 4563 UNION ALL SELECT 46779160, 29, 26.4, 50, 61, 8, 31, 15, 36, 40, 242, 4562 UNION ALL SELECT 46778595, 43, 28.4, 49, 47, 7, 28, 22, 27, 43, 224, 4555 UNION ALL SELECT 46779482, 34, 33.4, 64, 44, 10, 17, 27, 43, 52, 222, 4556 UNION ALL SELECT 46779650, 39, 37.4, 51, 62, 13, 31, 25, 31, 52, 224, 4551 UNION ALL SELECT 46780524, 33, 40.4, 60, 53, 18, 32, 31, 31, 39, 232, 4563 UNION ALL SELECT 46780634, 39, 35.4, 46, 44, 16, 33, 19, 31, 52, 242, 4557 UNION ALL SELECT 46781887, 24, 30.4, 54, 62, 13, 18, 29, 24, 52, 223, 4561 Go to the Tables and Views section and verify that the mkt_sls_table table was successfully created. Create the Amazon Redshift publish environment and a new Redshift table Staying in the same Amazon DataZone domain demo_cross_account_domain, choose Browse Project, to create an Amazon Redshift publish environment, select the Redshift_Publish_Project and create Redshift_Publish_Environment using the default data warehouse profile. To configure environment parameters, enter the name of your Amazon Redshift cluster or workgroup, specify the database name and enter the AWS Secrets Manager secret ARN for the Redshift cluster or workgroup. You need to make sure that the secret in Secrets Manager includes the following tags. These tags help Amazon DataZone implement proper access control so that only authorized users within the correct Amazon DataZone project and domain can access the Amazon Redshift resource: For Amazon Redshift cluster : DataZone.rs.cluster: <cluster_name:database name> For Amazon Redshift Serverless workgroup : DataZone.rs.workgroup: <workgroup_name:database_name> AmazonDataZoneProject: <projectID> AmazonDataZoneDomain : <domainID> For more information for creating redshift database user secret in secret manager, see Storing database credentials in AWS Secrets Manager. For more information for creating redshift database user secret in secret manager, see Storing database credentials in AWS Secrets Manager . Note that the database user you provide in Secrets Manager must have superuser permissions. Data publishers should work with the data administrator to get the details of the Redshift cluster or workgroup, database name, and secret ARN. The schema is optional. Choose Create Environment and wait for the process to complete. Verify that the environment is created successfully without errors. Browse the list of available projects and select Redshift_publish_project . Navigate to Redshift_publish_environment . Under Analytics tools, choose Amazon Redshift to open the Amazon Redshift query editor. Select the Redshift cluster that you want to connect, choose Save and then choose Create Connection using temporary credentials with your IAM identity. Create a new Redshift table. You can use the CTAS query to create a new table named rs_sls_tbl . Use the provided CTAS script, which creates a table with sample sales data in the datazone_env_redshift_publish_environment schema. CREATE TABLE "datazone_env_redshift_publish_environment"."rs_sls_tbl" AS SELECT 146776932 AS ord_num, 23 AS sales_qty_sld, 23.4 AS wholesale_cost, 45.0 as lst_pr, 43.0 as sell_pr, 2.0 as disnt, 12 as ship_mode,13 as warehouse_id, 23 as item_id, 34 as ctlg_page, 232 as ship_cust_id, 4556 as bill_cust_id UNION ALL SELECT 46776931, 24, 24.4, 46, 44, 1, 14, 15, 24, 35, 222, 4551 UNION ALL SELECT 46777394, 42, 43.4, 60, 50, 10, 30, 20, 27, 43, 241, 4565 UNION ALL SELECT 46777831, 33, 40.4, 51, 46, 15, 16, 26, 33, 40, 234, 4563 UNION ALL SELECT 46779160, 29, 26.4, 50, 61, 8, 31, 15, 36, 40, 242, 4562 UNION ALL SELECT 46778595, 43, 28.4, 49, 47, 7, 28, 22, 27, 43, 224, 4555 UNION ALL SELECT 46779482, 34, 33.4, 64, 44, 10, 17, 27, 43, 52, 222, 4556 UNION ALL SELECT 46779650, 39, 37.4, 51, 62, 13, 31, 25, 31, 52, 224, 4551 UNION ALL SELECT 46780524, 33, 40.4, 60, 53, 18, 32, 31, 31, 39, 232, 4563 UNION ALL SELECT 46780634, 39, 35.4, 46, 44, 16, 33, 19, 31, 52, 242, 4557 UNION ALL SELECT 46781887, 24, 30.4, 54, 62, 13, 18, 29, 24, 52, 223, 4561 Make sure that the rs_sls_tbl table is successfully created. Publish assets into the common business catalog In this step, you create and run the Amazon DataZone data sources for AWS Glue and Amazon Redshift . You will then publish the data assets from these data sources. The Amazon DataZone data sources allow you to connect to various data sources, including databases, data warehouses, and data lakes, and ingest metadata into Amazon DataZone. By creating and running these data sources, you can make your data available for analysis, transformation, and sharing within your organization. After the data sources are set up, you can publish the data assets from these sources to make them accessible to other users and applications. This process involves mapping the data assets to the appropriate business terms and metadata, making sure that the data is properly described and categorized. Add an AWS Glue data source to publish the new AWS Glue table. Stay signed in the producer account and Amazon DataZone console as a data publisher. Choose Select project from the top navigation pane and select the Glue_Publish_Project that you want to add the data source to. Select the Glue_Publish_Environment . Choose Create data source . Enter glue-publish-datasource as the name. Under Data source type , choose AWS Glue . Under Select an environment , select Glue_Publish_Environment . Under Data selection , select the AWS Glue database glue_publish_environment_pub_db , enter your table selection criteria as â*â, and then and choose Next . Leave all other setting as default and choose Next . For Run Preference , select Run on demand to ingest metadata from the specified AWS Glue tables into Amazon DataZone. Review and choose Create . After the data source has been created choose Run . The mkt_sls_table will be listed in the inventory and available to publish. Select the mkt_sls_table table and review the metadata that was generated. Choose Accept All if youâre satisfied with the metadata. Choose Publish Asset and the mkt_sls_table table will be published to the business data catalog, making it discoverable and understandable across your organization. Add an Amazon Redshift data source to publish the new Amazon Redshift table. Stay signed in the producer account and Amazon DataZone console as a data publisher. Choose Select project from the top navigation pane and select the Redshift_Publish_Project that you want to add the data source to. Choose the Redshift_Publish_Environment . Choose Create data source . Enter rs-publish-datasource as the name. Under Data source type , select Amazon Redshift . Under Select an environment , select Redshift_Publish_Environment . Under Redshift Credentials , enter the Redshift cluster and secret details provided by the data administrator. Under Data Selection, select the database dev and schema datazone_env_redshift_publish_environment . Keep other setting as default and choose Next . For Run Preference , select Run on Demand . Choose Save . After the data source is created, choose Run . The data source runs and the rs_sls_tbl will be listed in the inventory and available to publish. Select the rs_sls_tbl table and review the metadata that was generated. Choose Accept All if you are satisfied with the metadata. Choose Publish Asset and the rs_sls_table table will be published to the business data catalog. Create subscribe projects for AWS Glue and Amazon Redshift In this step, you create the projects for subscribing to AWS Glue and Amazon Redshift data assets within your Amazon DataZone domain. Sign in to the Amazon DataZone console as a data subscriber IAM user using the consumer account. Choose Associated domains and select the demo_cross_account_domain . Select the Open data portal link and sign in to the data portal. Choose Create New Project and create a project named Glue_Subscribe_Project for subscribing to the AWS Glue data assets. Create another project named Redshift_Subscribe_Project for subscribing to the Redshift data assets. Create AWS Glue and Amazon Redshift environment profiles In this step, you will set up the environment profiles and environments for AWS Glue and Amazon Redshift in your Amazon DataZone projects. This will allow you to connect and interact with resources across AWS accounts. The purpose of environment profiles in Amazon DataZone is to streamline the process of environment creation. By using environment profiles, you can preconfigure essential placement information such as AWS account and AWS Region. In this solution, you will configure environment profiles with placement information pointing to your consumer account. You will also create an Amazon DataZone environment from the profiles you are about to create. This will provision the necessary resources in the consumer account and establish the connections between the Amazon DataZone domain and the consumer account. After the environments are created, you can work with AWS Glue and Amazon Redshift assets seamlessly across different AWS accounts within your Amazon DataZone ecosystem. Create an AWS Glue profile and environment Stay signed in the consumer accountâs Amazon DataZone console as a data subscriber IAM, select the Environments tab and then choose Create environment profile . Configure the fields as follows: Name: Enter glue_subscribe-env-profile . Owner: The project where the profile is being created is selected by default in this field. Verify that itâs Glue_Subscribe_Project . Blueprint: Select Default Data Lake. AWS account parameters: Enter the consumer AWS account number and select the Region. Authorized projects: Select All projects. Publishing: Select Publish from any database. Choose Create Environment Profile. On the Create environment page, enter the following: Name : Enter glue_subscribe_environment . Verify that the Environment profile is set to glue_subscribe-env-profile . (Optional) Parameters : Enter the Producer glue db name, Consumer glue db name , and Workgroup name . Choose Create environment . It takes a few minutes for the environment to be created. Verify that the environment creation is successful without any errors. Create a Redshift environment profile and environment Staying in the consumer accountâs Amazon DataZone management console as a data subscriber IAM user, navigate to the Redshift_Subscribe_Project you created previously. Select the Environments tab and then choose Create environment profile . Configure the fields as follows: Name : Enter redshift_subscribe-env-profile . Owner : Verify that Project is set to Redshift_Subscribe_Project . Blueprint : Select Default Data Warehouse . Parameter set : Select Enter my own . AWS account parameters : Enter the consumer AWS account number and select the Region. Parameters : Select either Amazon Redshift Cluster or Amazon Redshift Serverless in the consumer account. AWS Secret ARN : Enter the AWS Secrets Manager secret ARN for the Redshift cluster or workgroup. You need to make sure that the secret in Secrets Manager includes the following tags. These tags help Amazon DataZone implement proper access control so that only authorized users within the correct Amazon DataZone project and domain can access the Amazon Redshift resource. AmazonDataZoneDomain : [ Domain_ID ] AmazonDataZoneProject : [ Project_ID ] For more information for creating redshift database user secret in secret manager, see Storing database credentials in AWS Secrets Manager . Note that the database user you provide in AWS Secrets Manager must have superuser permissions. Data publishers should work with the data administrator to get the details of the Redshift cluster or workgroup, database name, and secret ARN. Redshift cluster name : Enter the name of the Amazon Redshift cluster or Amazon Redshift Serverless workgroup. Database name : Enter the name of the database within the selected Amazon Redshift cluster or Amazon Redshift Serverless workgroup Authorized projects : Select All projects . Publishing : Select Publish any schema . Choose Create environment profile . Create an environment from this profile: Create an environment from this profile: Name: Enter redshift_subscribe_environment . Verify that the Environment profile is set to redshift_subscribe-env-profile . Choose Create Environment . It takes a few minutes for the environment to be created. Verify that the environment creation is successful without any errors. Subscribe to the AWS Glue and Redshift tables In this step, you will subscribe AWS Glue and Amazon redshift tables published by the data producer. Subscribe to the AWS Glue table Sign in to the Amazon DataZone console of the consumer account using the data subscriber credentials and navigate to the Glue_Subscribe_project you created previously. Search for the Market Sales Table in the Search bar. Select the Market Sales Table and choose Subscribe . In the Subscribe pop-up window, provide the following information: Project : Enter the name of the project that you want to subscribe to the asset. By default this will be Glue_Subscribe_Project . Enter a justification for your subscription request. Choose Subscribe . Switch to the data publisher role to approve the subscription request, then back to data subscriber after choosing Approve . Select the Glue_subscribe_project and choose Subscribed Assets . Verify that the Market Sales Table is added to your environment. Navigate to the Amazon Athena query editor using the link in the projectâs home page. Choose OPEN AMAZON ATHENA . You will now be automatically routed to the Athena console, make sure that the Amazon DataZone Environment is set to glue_subscribe_environment . For Database , select glue_subscribe_environment_sub_db . You should see the mkt_sls_table in the Tables list. Preview the table by choosing the three-dot menu next to the table name and selecting Preview Table Review the table preview results. You will be able to see all the sales related data from the mkt_sls_table Subscribe to the Redshift table Stay signed in to the Amazon DataZone management console as the data subscriber, Choose Select project from the top navigation pane and select the Redshift_Subscribe_project . Search for Sales Table in the search bar, and select the Sales Table . In the Subscribe pop-up window, provide the following information: Project : Enter the name of the project that you want to subscribe to the asset. By default this will be Redshift_Subscribe_Project . Enter a justification for your subscription request. Choose Subscribe . Switch back to the data publisher who is the producer of the Market Sales Table choose Approve . After the subscription request is approved, switch back to data subscriber. Select the Redshift_subscribe_project and choose Subscribed Assets . After the Sales Table is added to your environment, you can query the data in the table. Select the Amazon Redshift link in the right side panel of the project home page and navigate to the Amazon Redshift query editor. Select Open Amazon Redshift and the Redshift query editor v2 will open in a new tab. In the query editor, right-click your Amazon DataZone environmentâs Amazon Redshift cluster and select Create a connection . Select Temporary credentials using your IAM identity for authentication. If that authentication method isnât available, open Account settings by choosing the gear icon in the bottom left corner, choose Authenticate with IAM credentials and choose Save . Enter the name of the Amazon DataZone environmentâs database to create the connection. Choose Create connection . You can now view the Redshift table rs_sls_tbl in the datazone_env_redshift_subscribe_environment . Execute the following query to make sure the data is accessible SELECT * FROM "dev"."datazone_env_redshift_subscribe_environment"."rs_sls_tbl"; You will be able to preview the rs_sls_tbl which will show the sale data from the table. Clean up To avoid unnecessary future charges, follow these steps: Delete the Amazon DataZone project if you created it as part of this post. Delete the Amazon DataZone domain if you created it as part of this post. Delete the Redshift clusters and the redshift secrets in both the producer and consumer accounts if you created them as part of the post. Summary Organizations often face significant challenges when trying to share data products across multiple AWS accounts. These challenges stem from the complexity of configuring proper cross-account access permissions and roles while maintaining robust data governance and security controls. You can use the solution described in the post to publish and consume data across AWS accounts and make sure that reliable access and consistent data governance is in place. By combining the power of AWS Glue and Amazon Redshift, you can unlock valuable insights and accelerate your data-driven decision-making processes. In this post, you followed a step-by-step guide to set up cross-account data sharing using Amazon DataZone domain association. You learned how to publish data assets from a producer account. You also learned how to subscribe to and query the published assets from a consumer account. You can optionally use AWS Lake Formation access monitoring to view permissions and data access activities. AWS Lake Formation uses AWS CloudTrail for historical analysis and CloudTrail retains logs for 90 days by default. Now that youâre familiar with the elements involved in cross-account data sharing using Amazon DataZone and your choice of analytical tool, youâre ready to try it with multiple accounts. About the Authors Arun Pradeep Selvaraj is a Senior Solutions Architect at AWS. Arun is passionate about working with his customers and stakeholders on digital transformations and innovation in the cloud while continuing to learn, build and reinvent. He is creative, fast-paced, deeply customer-obsessed, and uses the working backwards process to build modern architectures to help customers solve their unique challenges. Connect with him on LinkedIn . Piyush Mattoo is a Senior Solution Architect for the Financial Services Data Provider segment at Amazon Web Services. Heâs a software technology leader with over a decade of experience building scalable and distributed software systems to enable business value through the use of technology. He has an educational background in Computer Science with a masterâs degree in computer and information science from University of Massachusetts. He is based out of Southern California and current interests include camping and nature walks. Mani Yamaraja is a Senior Customer Solutions Manager for Financial Services Data Provider segment at Amazon Web Services. He has over a decade long experience working with financial services customers enabling their digital transformation journey. Mani adopts a customer centric approach and provides technology solutions working backwards from customerâs business goals. He is passionate about the financial services industry and helps the customers accelerate their cloud based transformation using the proven mechanisms of AWS. 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https://grand-challenge.org/blogs/ | Blogs - Grand Challenge Grand Challenge Challenges Algorithms Reader Studies Blog Archives About Statistics Terms of Service Privacy Policy Help Grand Challenge Support Grand Challenge Documentation Sign In Register Blogs Filter Posts Title or Content Authors alexlemm amickan ammar.ammar anindo BramVanGinneken chrisvanrun chris.vanrun.diag coendevente crunch csangut dangvuquoc1993 dnschouten f.ciompi Gino harm.van.zeeland hlemij ionasec jamesmeakin.diag jmsmkn joeran.bosma keelinm KhrystynaFaryna kiranvaidhya koopman.t leslietessier01 LuukBoulogne mart.vanrijthoven meganschuurmans MiriamGroeneveld MiriamGroeneveldA mositemp pkcakeout renatedinnessen Roberto_Salgado_1234. siemdejong simongraham73 vilmabozgo Tags Research Software Engineering Tutorials Announcements Archives Algorithms Challenges Reader Studies News Research Blog 62 posts found December 2025 Cycle Report 5 Jan. 2026 by Miriam Groeneveld The December cycle was spent on finishing off some open issues, fixing some pesky bugs, and starting work on two issues that should improve the user experience. Read more about it in this blog post Read More Autumn Newsletter 2025 12 Dec. 2025 by renate dinnessen , Miriam Groeneveld and Chris van Run Welcome to the Autumn Edition of Grand Challenge Insights! Read More November 2025 Cycle Report 25 Nov. 2025 by Miriam Groeneveld , James Meakin , Chris van Run , Harm van Zeeland , Paul K. Gerke and Thomas Koopman DICOM support will go live soon, as all required features are implemented to use and view DICOM throughout the platform. This means DICOM can be used in Archives, Reader studies, Challenges and Algorithms in their original format. Read all about the details in this blogpost. Read More GCAPI 0.14.0 - breaking changes 25 Nov. 2025 by Chris van Run , Chris van Run and Miriam Groeneveld A new version of GCAPI has dropped: 0.14.0. It contains new features and some breaking changes. Read More October 2025 Cycle 24 Oct. 2025 by Miriam Groeneveld , Paul K. Gerke , James Meakin , Chris van Run , Thomas Koopman and Harm van Zeeland This cycle the work on supporting DICOM in our entire stack has continued. We have worked on de-identifying, uploading, viewing and provisioning DICOM, and this work will continue in the next cycle as well. We have fixed some bugs introduced in our last release, read all about it in our blogpost. Read More September 2025 Cycle 16 Sept. 2025 by Miriam Groeneveld The holidays are over, and MICCAI 2025 is on our doorstep. This year, we are happy to announce that two of our team members will be present at MICCAI to give a tutorial on hosting a challenge on Grand Challenge. The RSE-team has been busy the past cycle with the preparations for this tutorial and support for all the amazing challenges that were hosted on our platform. Besides that, the past two cycles a lot of work has been put into native DICOM support. Read all about it in this blog post. Read More Summer Newsletter 2025 25 Aug. 2025 by Megan Schuurmans , renate dinnessen , Chris van Run , Anne Mickan , Khrystyna Faryna and Miriam Groeneveld Welcome to the Summer Edition of Grand Challenge Insights! In this blog we will share the latest updates and highlights from our Grand Challenge community. Read More July 2025 Cycle Report 10 July 2025 by Miriam Groeneveld and Paul K. Gerke This cycle, we have enabled auto-save in our reader studies, updated our documentation page, and made the first steps towards adding support for native DICOM data. Read More April 2025 Cycle Report 28 April 2025 by Miriam Groeneveld During the April cycle, the RSE-team has worked on the reader study feature. We implemented budget caps for those reader studies that use interactive algorithms, thereby protecting us and our users from run-away costs. Participants in educational reader studies can view how well they did compared to other readers in the leaderboard section. Our client-side pathology viewer was missing a scalebar, so we added one this cycle. Read about these changes and more in our blogpost! Read More March 2025 Cycle Report 24 March 2025 by Miriam Groeneveld This past cycle, the RSE-team worked on enabling parameterisation of evaluations. This allows challenge organisers to test different evaluation options while re-using the inference results of the algorithms submitted as solutions to the challenge, minimising time and costs. We are giving challenge organisers insight into their issued and paid invoices, and have automated reminders for the unpaid invoices. Anne and Chris hosted another successful challenge workshop, and we'll release recordings from that workshop soon, in case you weren't able to attend. In our viewer, we have added options to use mask-growing tools, optimising workflow for segmentation tasks. Read the full blogpost for videos and additional information on these features. Read More February 2025 Cycle Report 18 Feb. 2025 by Miriam Groeneveld We are really happy to announce a new feature that allows optional inputs for algorithms. Until now, Grand Challenge algorithms required users to provide all inputs and receive all outputs. However, inputs often vary in real-world scenarios, and some algorithms can make meaningful predictions with just a subset of inputs. To address this, we've introduced optional inputs in this cycle, allowing algorithms to handle cases with varying or incomplete data, making the platform more flexible and adaptable. Our challenge organisers will benefit from our new task list feature, which will show what the next step in their challenge set-up is. We hope this will make your experience of hosting your challenge on our platform even better! We have also resolved some performance issues throughout the application, to provide a smooth and responsive experience. Read More Challenge Hosting Masterclass 14 Feb. 2025 by Anne Mickan Join us for a hands-on workshop to gain practical experience with setting up a challenge. Read More December 2024 Cycle Report 9 Dec. 2024 by Miriam Groeneveld , Anne Mickan , Chris van Run , James Meakin and Paul K. Gerke In the past cycle, we have implemented a new pricing schema, to give challenge organizers more control over their costs. We also expanded the number of supported GPU's for both challenge organizers and algorithm owners. Read the blogpost for more details and to find out about other improvements on our platform. Read More Autumn newsletter 2024 14 Nov. 2024 by Megan Schuurmans , Chris van Run , Anne Mickan , Khrystyna Faryna , Miriam Groeneveld and Vilma Bozgo Welcome to the Autumn newsletter of Grand Challenge! Read More November 2024 Cycle Report 5 Nov. 2024 by Miriam Groeneveld , Chris van Run , Paul K. Gerke , Anne Mickan , James Meakin , Ammar Ammar and Thomas Koopman This cycle the RSE-team has made it easier to create a grand-challenge ready algorithm container by providing an example pack, catered to your algorithm. We made some changes to our markdown editor, to ease the creation of writing custom pages for your challenge. And preparations are in full swing for our first challenge workshop this Thursday. Read all about it and more in our blogpost! Read More MICCAI 2024 18 Oct. 2024 by Miriam Groeneveld As MICCAI 2024 has drawn to a close, we would like to take the opportunity to share some insights on this year's challenges hosted on our platform. Check out the blogpost if you want to know more about which challenges were presented at MICCAI 2024, how our platform was utilized and how we prepare for next year's event. Read More September 2024 Cycle Report 1 Oct. 2024 by Chris van Run and Miriam Groeneveld Introducing Interactive Algorithms & Enhanced Signup Features! We are excited to announce a new, groundbreaking feature: interactive algorithms! This feature allows readers to use algorithms in the context of a reader study. By calling on these algorithms for assistance, you can enhance both the speed and accuracy of your annotations. Weâve also enhanced the signup process for challenges. You can now gather more detailed information from participants at the point of registration, making it easier to tailor your challenge to specific needs and ensure the best possible engagement. To dive deeper into how these new features can benefit you, check out the full details in the blog post. Read More Challenge Hosting Masterclass 30 Sept. 2024 by Anne Mickan and Chris van Run Join us for a hands-on workshop to gain practical experience with setting up a challenge. Read More July 2024 Cycle Report 22 July 2024 by Paul K. Gerke , Anne Mickan , Ammar Ammar and Harm van Zeeland In July we developed a new feature for Cirrus which allows editors to load registration data for images in the viewer. We included support displacement fields and affine transform registrations. These can be assigned to images using the view-content section in reader studies, archive items, or algorithms. Read More Summer newsletter 2024 5 July 2024 by Megan Schuurmans , Chris van Run , Daan Schouten , Khrystyna Faryna , Miriam Groeneveld and Vilma Bozgo Welcome to the summer newsletter of Grand Challenge! Read More June 2024 Cycle Report 21 June 2024 by Miriam Groeneveld and Chris van Run In this cycle, we introduced the capability to update containers and models separately for both Evaluation methods and Algorithms, streamlining and accelerating the process of updating your model or ground truth. For challenges utilizing multiple metrics, you now have the flexibility to exclude specific metrics from the ranking method while still displaying them. The Archive item detail page has been revamped, providing more information on the associated content and algorithm jobs. We also added a status page that displays the various components of grand-challenge.org and indicates if any component has issues. In our viewer, we implemented labeling and highlighting of annotations within the client-side viewer. If you often struggle to locate point annotations in 3D images, you can now enable a bounding box rendition around these points, enhancing their visibility from all orientations. Read More May 2024 Cycle Report 13 May 2024 by Miriam Groeneveld With this release, we introduce dependent phases as part of a Challenge. This is a gate-keeping feature, where submissions to a phase are only possible following a succesful submission to a parent phase. We now provide the option to name display sets and archive items, to ease identification. We extended the functionality and squashed bugs in the client-rendered pathology viewer, making it a fully-supported feature of our platform. And last, when viewing an algorithm result in Cirrus, you can easily link back to the correct algorithm result on Grand Challenge. Read the blog for more details! Read More April 2024 Cycle Report 8 April 2024 by Miriam Groeneveld and Chris van Run This cycle, the RSE team has extended the usability around images with a time component. Where before, you only had the option to select different time frames, you can now play as if the image was a video. You can set playback to once or continous, have the video loop or play in ping-pong style, and you can set the playback speed. For 3D images, we have added a configuration option that allows you to set the initial slice on which an image should be shown. A configuration option in your profile now supports instant email notifications, which is useful when you want to be notified of failed image uploads or other issues instantly, rather than once a day. We also further extended the support for annotating images in our client-side pathology viewer. Read More Spring newsletter 2024 8 April 2024 by Anne Mickan , Chris van Run , Vilma Bozgo , Megan Schuurmans , Daan Schouten and Khrystyna Faryna Spring newsletter 2024 Read More March 2024 Cycle Report 4 March 2024 by Miriam Groeneveld The end of the March Cycle will bring you some shiny new features in both Grand Challenge and our viewer. In Reader Studies, you can now send direct messages to your readers. We added an option to Reader Study questions that allows you to verify that a user intentionally left an answer blank. We added support for viewing PDF and Vega Lite charts in dedicated view items, giving you full control over the data displayed in your Reader Stusy, Archive or Algorithm results. We also extended the support for Three-point angle annotations, added to Reader Studies last cycle, to be used in Archives and Algorithm results. Read the blog for more details! Read More Organizing a medical imaging challenge in 2020s: practical considerations 5 Feb. 2024 by Khrystyna Faryna Organizing a deep learning challenge in medical imaging is a commendable initiative, as it can help drive advancements in the field and bring together researchers and practitioners to tackle important healthcare problems. However, there are several key considerations you should keep in mind to ensure the success and integrity of the challenge. Read More January 2024 Cycle Report 29 Jan. 2024 by Miriam Groeneveld The RSE team started the year with exciting updates! We've shifted algorithm job and challenge evaluation execution to Amazon SageMaker Training, removing one-hour time limits for algorithms. Editing and creating display sets is now easier, reducing reliance on our API. Cirrus now supports seamless display of videos, thumbnails, and text alongside medical images. We've improved 3D image display, eliminating the need to navigate after changing orientation. In educational reader studies, we introduced an "Instant Verification" option for faster participation. Users can now set default colors for reader study questions. Check out our blog for more details on these enhancements! Read More December 2023 Cycle Report 3 Jan. 2024 by Miriam Groeneveld Our latest blog post brings thrilling enhancements to boost your experience! Challenge editors can open submission logs for direct participant access. The revamped pathology viewer now supports overlays with LUT support. Algorithm pages showcase Challenge performance insights, and job limits extend beyond one hour. To ensure responsible use, algorithm editors now face restrictions. Discover the efficient 3-point angle annotation for a more user-friendly approach. Dive into the blog for a closer look at these game-changing updates! Read More Winter newsletter 2023 21 Dec. 2023 by Megan Schuurmans , Daan Schouten , Khrystyna Faryna , Miriam Groeneveld , Vilma Bozgo , Chris van Run , Anne Mickan and Siem de Jong Welcome to the Winter Edition of Grand Challenge Insights! Read More November 2023 Cycle Report 27 Nov. 2023 by Miriam Groeneveld , Paul K. Gerke , Chris van Run , Harm van Zeeland , James Meakin and Anne Mickan This cycle we continued our work on the client-side pathology viewer. We also implemented a first version of a Intensity-over-time chart feature. For challenge organizers, it is now possible to send messages directly to their participants! Check out the blog for these features and more! Read More October 2023 Cycle Report 16 Oct. 2023 by Miriam Groeneveld , Paul K. Gerke , Chris van Run , James Meakin and Harm van Zeeland In this cycle, we worked on improving the performance of our reader studies, enabled setting the viewing depth of annotations in 3D images, and continued our work on the client-side pathology viewer. One of our team members attended Miccai 2023 to meet with challenge organizers and receive feedback. Read the blog for more details! Read More Autumn newsletter 2023 18 Sept. 2023 by Megan Schuurmans , Khrystyna Faryna , Daan Schouten , Chris van Run and Anindo Saha Welcome to the Autumn Edition of Grand Challenge Insights! Read More September 2023 Cycle Report 12 Sept. 2023 by Miriam Groeneveld , Chris van Run , Paul K. Gerke and Harm van Zeeland This cycle, the RSE team dedicated significant efforts to support various MICCAI 2023 challenges. Additionally, we enhanced the Cirrus viewer's usability, enabling seamless switching between different hanging protocols within the viewer. Furthermore, we addressed a prior issue, reinstating the functionality to switch between the polygon and mask editor. In the context of mask creation, we introduced a new feature allowing you to conveniently hide the annotation you're editing for accuracy verification using a straightforward keybinding. Explore our latest blog post for more details! Read More August 2023 Cycle Report 10 Aug. 2023 by Miriam Groeneveld , Paul K. Gerke and Chris van Run This cycle the RSE team has started working on a new, experimental feature: Implementing a client-side viewer, for now specifically for pathology images. This should provide a faster, more responsive experience when viewing these images. Furthermore, the GitHub repository integration has been improved and bugs squashed. Challenge organizers can now combine results from different phases to create an overall leaderboard. Finally, we have added support for viewing overlapping semantic segmentations. Read More July 2023 Cycle Report 13 July 2023 by Miriam Groeneveld and James Meakin This cycle we have brought some new additions to the reader study questions, check it out! Read More May 2023 Cycle Report 30 May 2023 by Miriam Groeneveld This cycle, the RSE team has made improvements for pathology. It is now possible to upload DICOM-WSI files, they will be converted to tiff. Segmentations as tiff files are now supported and will be validated according to the segmentation interface. The annotation statistics plugin has had an overall make-over, with progress bar and caching improving general usability. Other features include a 3D brush for creating and editing masks, and improvements to the admin page for challenge organizers. Read More Spring newsletter 2023 25 May 2023 by Megan Schuurmans , Anne Mickan , Kiran Vaidhya Venkadesh , Khrystyna Faryna , Daan Schouten , Gino Jansen and Miriam Groeneveld As the first rays of spring sunshine warm our faces and the flowers begin to bloom, we are excited to bring you the latest news on the grand challenge ahead. Read More April 2023 Cycle Report 26 April 2023 by Chris van Run , Paul K. Gerke and Miriam Groeneveld Cycle report of the research-software engineers of April 2023, including constrained number inputs, browser-native history support when context switching, and editing annotation metadata Read More March 2023 Cycle Report 20 March 2023 by Chris van Run and Miriam Groeneveld Cycle report of the research-software engineers of March 2023, including: more statistics on Grand Challenge, updated algorithm-job permissions on Grand Challenge, upgrade the CIRRUS backbone MeVisLab version to v3.6, added a new reader-study workflow for accepting and rejecting findings in both CIRRUS and Grand Challenge. Read More Winter newsletter 2023 20 Feb. 2023 by Megan Schuurmans , Anne Mickan , Khrystyna Faryna , Kiran Vaidhya Venkadesh , Gino Jansen , Daan Schouten , James Meakin and Miriam Groeneveld First of all, happy new year from us to you! Read More January 2023 Cycle Report 15 Feb. 2023 by Miriam Groeneveld This cycle work was done to make algorithm result viewing faster by reusing Cirrus sessions. We also made it possible to select existing images for trying out an Algorithm, creating a Display Set in Reader Studies and creating Archive Items. Read More December 2022 Cycle Report 10 Jan. 2023 by Miriam Groeneveld and Chris van Run In this year's first release, we added the option to reuse existing images to run an algorithm on or create an archive item or display set. Previously this was only possible with the python client. We extended our annotation types with the ellipse annotation, to be used in Reader studies or Algorithms. Under the hood, we are working on improving the viewing of pathology images, making them faster and more error-proof. To improve the reliability and ease of use of the GC-API client, we have added retries to certain requests in the client. This should reduce the number of errors that need to be handled by the user. Read More November 2022 Cycle Report 29 Nov. 2022 by Miriam Groeneveld A new tool and some bug fixes for the viewer in this cycle, while most effort has gone to the development of Grand Challenge Connect, presented at RSNA! A lot of interesting and exciting developments, so make sure to check out the dedicated blogpost. Read More Grand Challenge at RSNA 2022 23 Nov. 2022 by James Meakin Radboudumc will be at RSNA to present the latest developments of Grand Challenge, building on Amazon HealthLake Imaging. Visit us for a hands-on demo at the AWS Booth (6758) between 27-30 November 2022 at McCormick Place in Chicago. Read More CoNIC Challenge: Pushing state-of-the-art for automatic nuclear recognition 17 Nov. 2022 by Khrystyna Faryna , Simon Graham , Mostafa Jahanifar and Dang Vu CoNIC Challenge 2022 Read More October 2022 Cycle Report 1 Nov. 2022 by Miriam Groeneveld A relatively small update as we are working hard on something new and exciting, which will be revealed next cycle! Read More Autumn Newsletter 2022 1 Nov. 2022 by Megan Schuurmans , Anne Mickan , Daan Schouten , Kiran Vaidhya Venkadesh , Khrystyna Faryna , Gino Jansen and Miriam Groeneveld Hi all! With this newsletter we want to update you on all progress made for Grand Challenge: upcoming Challenges, interesting new Algorithms and Blogposts, leaderboards of finished Challenges and new features of Grand Challenge. Read More When AI meets the TILs: results from the TIGER challenge 24 Oct. 2022 by Francesco Ciompi , Anne Mickan , Megan Schuurmans , Alexander Lemm , Mart van Rijthoven , Leslie Tessier , Witali Aswolinskiy and Bram van Ginneken Final results from the TIGER challenge. Read More How to run a challenge? MICCAI webinar on Oct 25th 2022 20 Oct. 2022 by Anne Mickan , Kiran Vaidhya Venkadesh and Bram van Ginneken The MICCAI Special Interest Group (SIG) for Challenges is hosting a webinar on How to run a challenge? on Oct 25th, 2022 at 1 pm GMT. Bram van Ginneken, Kiran Vaidhya Venkadesh, and Anindo Saha will present how to use Grand Challenge for organizing high-profile challenges. Jake Albrecht from Sage will present tips for challenge organizers on how to define a successful community challenge. Join us! Free registration is available at https://bit.ly/BIAS1025 Read More STOIC2021 results webinar 12 Oct. 2022 by Luuk Boulogne and Bram van Ginneken On October 18th at 3pm CEST, the winners of STOIC2021 will receive their prizes and present their solutions! Read More September 2022 Cycle Report 26 Sept. 2022 by Miriam Groeneveld Highlights include the addition of an angle tool, a rotate tool for pathology images and displaying probability scores for annotations. Read More August 2022 Cycle Report 22 Aug. 2022 by Miriam Groeneveld Highlights include the option to send an algorithm result to a reader study, using non-image type values in reader studies, the option to send feedback and more. Read More June 2022 Cycle Report 8 Aug. 2022 by Miriam Groeneveld , Chris van Run , Harm van Zeeland , James Meakin , Paul K. Gerke and Anne Mickan Highlights include improvements in type 2 challenge submission workflow, creating non-binary masks, adding static annotations to reader studies and more. Read More Summer Newsletter 2022 8 July 2022 by Megan Schuurmans , Anne Mickan , Kiran Vaidhya Venkadesh , Khrystyna Faryna , Daan Schouten and Joeran Bosma Hi all! With this newsletter, we want to update you on all progress made for Grand Challenge: upcoming Challenges, interesting new Algorithms, Blogposts, leaderboards of finished Challenges, and new features of Grand Challenge. Read More May 2022 Cycle Report 5 July 2022 by Miriam Groeneveld , Harm van Zeeland and Chris van Run Highlights include viewing a reader's answers, providing default answers for questions in a reader study, starting a reader study at a specific display set and more. Read More AIROGS Challenge Report: AI models can be used for glaucoma screening, but do they know when they cannot? 10 May 2022 by Coen de Vente , Bram van Ginneken , Megan Schuurmans , Clarisa Sanchez and Hans Lemij The rationale behind the Artificial Intelligence for RObust Glaucoma Screening (AIROGS) Challenge, an overview of its results and our experiences with Grand Challenge's new Type 2 challenges. Read More Spring Newsletter 2022 11 April 2022 by Megan Schuurmans , Anne Mickan , Joeran Bosma , Kiran Vaidhya Venkadesh , Bram van Ginneken , Khrystyna Faryna and Daan Schouten Hi all! With this newsletter we want to update you on all progress made for Grand Challenge: new features, interesting new blogposts and algorithms, upcoming challenges and the leaderboards of finished challenges. Read More Can AI predict breast cancer recurrence via automated quantification of tumor-infiltrating lymphocytes? 6 March 2022 by Francesco Ciompi , Anne Mickan , Bram van Ginneken , Alexander Lemm , Roberto Salgado and Megan Schuurmans TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) breast cancer histopathology slides. With TIGER, we released a training set of 390 whole-slide images and a total award of $13,000 in AWS Credits, which will be awarded to the winning teams. Together with its participants, we aim to find the best AI-based solutions for automating the assessment of the TILs and produce a âTILs scoreâ that can predict the recurrence of breast cancer. Read More Can you predict who will develop severe COVID-19 from a chest CT scan? 16 Dec. 2021 by Bram van Ginneken , Alexander Lemm , Luuk Boulogne and Anne Mickan Last week, we opened STOIC2021: A COVID-19 AI challenge with 10,000 CT scans. Together with its participants, we aim to find the best solution for predicting who will develop severe COVID-19 from a chest CT scan. We will make the final solution easily accessible for everyone. In total, $20,000 in AWS Credits will be awarded to the winning teams. Read More AI challenges, data, and algorithms 4 March 2021 by Bram van Ginneken Presentation by Bram van Ginneken held during the European Congress of Radiology 2021 on the how and why of challenges in medical image analysis. Read More Why Challenges? 14 Feb. 2021 by Keelin Murphy and Bram van Ginneken This is an updated version of blog post made years ago when we launched our platform grand-challenge.org. It makes the case for challenges and provides some advice for those interested in setting up their own challenge. Read More How to build a global, scalable, low-latency, and secure machine learning medical imaging analysis platform on AWS 24 Aug. 2020 by Razvan Ionasec , Bram van Ginneken and James Meakin It is hard to imagine the future for medical imaging without machine learning (ML) as its central innovation engine. Countless researchers, developers, start-ups, and larger enterprises are engaged in building, training, and deploying machine learning solutions for medical imaging that are posed to transform todayâs medical workflows and the future value of imaging in diagnosis and treatment. Read More Grand Challenge About Challenge Policy & Pricing Support & Documentation Statistics Status Policies Terms of Service Privacy Policy Developers API Documentation API Schema Developer Documentation Sponsors © 2012-2026 | 2026-01-13T09:29:11 |
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https://aws.amazon.com/blogs/big-data/category/analytics/amazon-sagemaker-unified-studio/ | Amazon SageMaker Unified Studio | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon SageMaker Unified Studio Navigating architectural choices for a lakehouse using Amazon SageMaker by Lakshmi Nair and Saman Irfan on 12 JAN 2026 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics Permalink Comments Share Over time, several distinct lakehouse approaches have emerged. In this post, we show you how to evaluate and choose the right lakehouse pattern for your needs. A lakehouse architecture isnât about choosing between a data lake and a data warehouse. Instead, itâs an approach to interoperability where both frameworks coexist and serve different purposes within a unified data architecture. By understanding fundamental storage patterns, implementing effective catalog strategies, and using native storage capabilities, you can build scalable, high-performance data architectures that support both your current analytics needs and future innovation. Use Amazon SageMaker custom tags for project resource governance and cost tracking by David Victoria , Ahan Malli , and Rohit Srikanta on 08 JAN 2026 in Advanced (300) , Amazon SageMaker , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share Amazon SageMaker announced a new feature that you can use to add custom tags to resources created through an Amazon SageMaker Unified Studio project. This helps you enforce tagging standards that conform to your organizationâs service control policies (SCPs) and helps enable cost tracking reporting practices on resources created across the organization. In this post, we look at use cases for custom tags and how to use the AWS Command Line Interface (AWS CLI) to add tags to project resources. AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale by Larry Weber on 07 JAN 2026 in Amazon EMR , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon OpenSearch Service , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , AWS Glue , AWS Lake Formation , AWS re:Invent , Intermediate (200) Permalink Comments Share re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, [âŠ] Unifying governance and metadata across Amazon SageMaker Unified Studio and Atlan by Karan Singh Thakur, Satabrata Paul , Divij Bhatia , and Leonardo Gomez on 22 DEC 2025 in Advanced (300) , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, we show you how to unify governance and metadata across Amazon SageMaker Unified Studio and Atlan through a comprehensive bidirectional integration. Youâll learn how to deploy the necessary AWS infrastructure, configure secure connections, and set up automated synchronization to maintain consistent metadata across both platforms. How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker by Avinash Erupaka , Radhika Kashyap , and Modood Alvi on 12 DEC 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows. Orchestrating data processing tasks with a serverless visual workflow in Amazon SageMaker Unified Studio by Suba Palanisamy , Kamen Sharlandjiev , Gal Heyne , Yuhang Huang , Vinod Jayendra , and Vasudevan Venkataramanan on 25 NOV 2025 in Advanced (300) , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, we show how to use the new visual workflow experience in SageMaker Unified Studio IAM-based domains to orchestrate an end-to-end machine learning workflow. The workflow ingests weather data, applies transformations, and generates predictionsâall through a single, intuitive interface, without writing any orchestration code. Getting started with Amazon S3 Tables in Amazon SageMaker Unified Studio by David Pasha , Sakti Mishra , Debu Panda , and Vivek Shrivastava on 19 NOV 2025 in Advanced (300) , Amazon S3 Tables , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, you learn how to integrate SageMaker Unified Studio with S3 Tables and query your data using Amazon Athena, Amazon Redshift, or Apache Spark in EMR and AWS Glue. Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Enhanced search with match highlights and explanations in Amazon SageMaker by Ramesh H Singh , Pradeep Misra , Rajat Mathur , Ron Kyker , and Kyle Wong on 04 NOV 2025 in Advanced (300) , Amazon SageMaker Unified Studio , Announcements , Technical How-to Permalink Comments Share Amazon SageMaker now enhances search results in Amazon SageMaker Unified Studio with additional context that improves transparency and interpretability. The capability introduces inline highlighting for matched terms and an explanation panel that details where and how each match occurred across metadata fields such as name, description, glossary, and schema. In this post, we demonstrate how to use enhanced search in Amazon SageMaker. Use trusted identity propagation for Apache Spark interactive sessions in Amazon SageMaker Unified Studio by Aarthi Srinivasan and Palani Nagarajan on 31 OCT 2025 in Advanced (300) , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, we provide step-by-step instructions to set up Amazon EMR on EC2, EMR Serverless, and AWS Glue within SageMaker Unified Studio, enabled with trusted identity propagation. We use the setup to illustrate how different IAM Identity Center users can run their Spark sessions, using each compute setup, within the same project in SageMaker Unified Studio. We show how each user will see only tables or part of tables that theyâre granted access to in Lake Formation. â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? 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https://www.perensoft.com/ui-ux-design-services/ | --> --> Top UI UX Design Company India | UI UX Design Services Home Services Cloud Computing & DevOps AWS CI CD Containerization Infrastructure as Code (IaC) Cost Optimization Well-Architected Review UI UX Design Mobile App Development Custom Software Development Digital Marketing About Us Case Studies Blogs Career Get a Quote Top-Notch UI UX Design Services We transform ideas into exceptional digital products that are beautiful, functional, and user-friendly. Every pixel is crafted to engage your audience and drive results. Letâs Redesign in 2 Weeks Seamless User Journeys Responsive & Adaptive UX Enterprise-Grade UI/UX Design Services for Digital Transformation We go beyond surface-level design to build experiences that drive real results. Every wireframe, prototype, and design system our team delivers is crafted for effortless implementation, that ensures consistency and performance across all devices. From day one, we collaborate closely with your team to create designs visually appealing and technically sound. It reduces rework, increases launches, and delivers interfaces users love from the first interaction. Our process starts with optimizing user flows for maximum clarity, then crafting future-proof designs that are rooted in research. We deliver production-ready assets that balance aesthetics with functionality that cuts onboarding time and gives effortless adoption. We refine interactions until they feel natural and purposeful, by testing real user behavior. It transforms complex processes into simple, delightful experiences that drive lasting results. Book a Free Strategy Session End-to-End UI UX Design Services As your trusted UI UX design partner, we specialize in creating simple and unique digital experiences that solve real problems. User Research & Strategy We analyze user behavior, pain points, and market trends to build a data-driven UX strategy. Our research makes your product align with real user needs while supporting your business goals. Wireframing & Prototyping Before development begins, we create interactive prototypes to test flows and refine usability. It saves time and costs by identifying improvements early in the design process. UI/UX Design for Web & Mobile Apps From sleek dashboards to mobile-friendly interfaces, we design scalable, responsive UIs that work across devices. Every pixel is optimized for both aesthetics and functionality. Design Systems & UI Components We build reusable design systems with consistent components, speeding up development and making brand cohesion. It keeps your product scalable and maintainable. UX Audit & Optimization Our experts review existing products to identify UX gaps and boost conversion rates. We deliver actionable insights to increase usability and remove friction. Accessibility & Inclusive Design We ensure your product meets WCAG/ADA compliance, broadening your audience while mitigating legal risks. Accessible design means better usability for all. Need UI UX Engineers Who Speak Dev? Get UI UX designers who truly understand React component architecture, Figma design tokens, and scalable design systems. Hire Tech-Savvy Designers Core Principles of High-Performing UI UX Design Great design goes beyond aesthetics. It solves problems intuitively. These are the things we look for in every project to guarantee usability, accessibility, and business outcomes. Intuitive Navigation We design interfaces where users find what they need within 3 clicks by using clear visual hierarchies and predictable patterns. It reduces frustration and cuts support requests by up to 30%. Lightning-Fast Performance Slow interfaces kill conversions. We optimize images, simplify animations, and prioritize speed to keep load times under 3 seconds. Research shows it can reduce bounce rates by 53%. Mobile-First Responsive With 60%+ traffic from mobile, we start designs with small screens first, for touch-friendly targets and adaptive layouts. Your brand looks sharp on any device without functionality compromises. Accessibility by Default WCAG-compliant contrast, keyboard navigation, and screen reader support are built into every project from day one. Accessible design improves usability for all users while mitigating legal risks. Action-Oriented Visuals Every color, icon, and animation guides users toward goals like purchases or sign-ups. Strategic visual cues have boosted client conversion rates by up to 25%. Data-Backed Decisions We test designs with A/B experiments and heatmaps, not hunches, to validate what works. It has reduced cart abandonment by 18% for e-commerce clients. Scalable Architecture Our modular designs adapt to new features, languages, or tech stacks without costly reworks. Advance your product by keeping development agile. Consistent Design Systems Reusable UI components and documentation maintain brand cohesion by speeding up development. Clients uses our systems launch features 40% faster with fewer inconsistencies. Step-by-Step Framework for Exceptional UI UX We blend research, creativity, and technical precision in our UI/UX design process to deliver intuitive, high-impact digital experiences that users love and businesses trust. Discovery & Research We analyze your users, market trends, and business goals through surveys, interviews, and competitor audits to build a data-driven UX strategy. Wireframing & Prototyping Low-fidelity wireframes and interactive prototypes help us test user flows early BY saving time and costs before development begins. UI Design & Branding Pixel-perfect UI design services transform wireframes into visually stunning interfaces that are aligned with your brand identity and accessibility standards. Usability Testing Real users interact with prototypes while we gather feedback to refine navigation, clarity, and functionality by eliminating UX friction points. Development Handoff We provide developer-friendly design systems, specs, and assets like Figma files, style guides for seamless integration into your DevOps pipeline. Launch & Optimization Post-launch, we monitor analytics and conduct A/B tests to continuously improve conversions and adapt to user behavior. Our Go-To Design Platforms and Design Tools We Use As a leading UI UX design agency, we use industry-standard tools to ensure pixel-perfect designs, smooth collaboration, and developer-friendly handoffs. Figma Sketch Adobe XD InVision Zeplin Notion Adobe Photoshop Balsamiq Adobe After Effects Miro Adobe Illustrator Expert UI UX Design Across Key Sectors As a flexible UI UX design agency, we modify our skills to satisfy the particular requirements of enterprise, e-commerce, and SaaS clients. Healthcare & Medicine Banking & Finance Travel & Tourism Logistics & Procurement Real Estate Gaming Restaurants Media & Entertaintment E-Learning E-Commerce Social Network SaaS Why Partner With Perensoft for UI UX Design Services? We have a team of expert professionals that develop interfaces that look amazing and function flawlessly by fusing technological expertise with user-centred design. Industry-Specific Expertise We donât offer generic solutions. Our team is prominent in e-commerce, SaaS, finance, and healthtech. We understand the detailed scope of your project and speak the language of your industry. Data-Backed Design Decisions Every pixel has a purpose. We use heatmaps, A/B testing, and user analytics to validate designs that drive real business results, not just aesthetic appeal. Seamless Developer Collaboration Our Figma-to-code handoffs are developer-friendly with clean design systems and specs that integrate smoothly into your DevOps workflow. It saves time and reduces friction. Advance Scalability We build modular design systems that grow with your product, whether youâre adding features, expanding to new markets, or upgrading your tech stack. Built In Accessibility WCAG-compliant designs arenât an afterthought; we integrate accessibility into every project from day one. It allows inclusivity by mitigating legal risks. End-to-End Ownership We own the entire UI UX design process, From initial research to post-launch optimization to get no fragmented teams or misaligned priorities. Tired of Users Abandoning Your App? Our UI/UX fixes reduce bounce rates by 40%+. Letâs diagnose your issues. Get free consultation. Find My UX Leaks Learn, Grow and Master Our Latest Articles As a leading UI/UX design agency, we share proven strategies, case studies, and best practices to help you build seamless, high-performing interfaces. See All Blogs --> On-Premise vs Cloud: What You Need to Know Before You Choose Compare on-premises and Cloud solutions to find the best fit for your business. Explore costs,… Read More How Much Does It Really Cost to Build a SaaS Application on AWS? Discover the true cost of building a SaaS app on AWS. Learn what factors affect… Read More See All Blogs --> Frequently Asked Questions Whatâs the difference between UI and UX design? User Interface Design, or UI, is related with the colors, buttons, and layouts of your product. While, UX – User Experience design is about how it works in navigation, speed, and ease of use. At Perensoft , we excel at both, crafting interfaces that are visually stunning and intuitively functional. What design tools do you use for UI UX? Our designers use industry-standard tools for wireframing and prototyping, such as Adobe XD, Figma, and Sketch. Plus, they also use After Effects for animations and Miro for group discussion. It gives seamless workflows from concept to final design. Can you design mobile apps and responsive web interfaces? Yes, we specialize in mobile app development and user-focused UI UX design and create fully responsive web interfaces that adapt flawlessly to all devices because 60%+ users browse on phones today. Do Parensoft offer branding along with UI/UX design? Of course, our end-to-end design services include branding logos, style guides alongside UI UX to give a cohesive identity. Strong branding and great UX gives memorable customer experiences. How long does a UI/UX project take? Deadlines vary on project complexity, but a standard website or app redesign takes 4â8 weeks. Our team shall share a detailed roadmap after discussing your goals. How to Hire UI UX Designers from Parensoft? It’s very simple. You just have to book a free consultation, share your needs, review our portfolio, and weâll match you with top UI UX designers from our vetted team that matches your requirements. Do you provide animated or interactive prototypes? Yes, we design high-fidelity interactive prototypes that test user processes and bring your product to life before it is developed. These prototypes include clickable demos and subtle animations. Why is Parensoft the Best UI UX Design Company in India? Our team consists of vetted designers that have 10+ years of experience, completed 200+ successful projects, and always focuses on data-driven designs, we blend creativity with technical precision. Plus, most of our clients are with us due to our transparent process and dedicated support. Drop Us a Line, Weâll Handle the Rest First Name * Last Name * Work Email Address * Phone Number * Brief Description * Select Your Budget* $1K to $5K $5K to $10K $10K+ Not Sure Input this code: Driving digital transformation through advanced cloud, DevOps and AI solutions that expands with your business ambitions. 3 - Shastri-Nagar, Gandhigram, Rajkot -360007 (Gujarat - INDIA) +91 - 7990573157 Our Company Home About Us Case Studies Blogs Career Contact Us Services Cloud Computing & DevOps AWS CI/CD Containerization Infrastructure as Code (IaC) Cost Optimization Aws Well Architected Framework UI UX Design Mobile App Development Custom Software Development Digital Marketing 2026 Perensoft Solutions. All Rights Reserved. Terms & Condition Privacy Policy Site Map | 2026-01-13T09:29:11 |
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https://aws.amazon.com/blogs/big-data/category/post-types/thought-leadership/page/2/ | Thought Leadership | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Thought Leadership Power analytics as a service capabilities using Amazon Redshift by Sandipan Bhaumik and Sain Das on 17 APR 2024 in Amazon Redshift , AWS Data Exchange , Best Practices , Technical How-to , Thought Leadership Permalink Comments Share Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. The AaaS model accelerates data-driven decision-making through advanced analytics, enabling organizations to swiftly adapt to changing market trends and make [âŠ] Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1 by Raghavarao Sodabathina , Brittany Ly , Hang Zuo , and Shwetha Radhakrishnan on 08 JAN 2024 in Intermediate (200) , Kinesis Data Streams , Technical How-to , Thought Leadership Permalink Comments Share Weâre living in the age of real-time data and insights, driven by low-latency data streaming applications. Today, everyone expects a personalized experience in any application, and organizations are constantly innovating to increase their speed of business operation and decision making. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being [âŠ] Implement Apache Flink real-time data enrichment patterns by Luis Morales and Lorenzo Nicora on 15 NOV 2023 in Advanced (300) , Amazon Managed Service for Apache Flink , Analytics , Thought Leadership Permalink Comments Share You can use several approaches to enrich your real-time data in Amazon Managed Service for Apache Flink depending on your use case and Apache Flink abstraction level. Each method has different effects on the throughput, network traffic, and CPU (or memory) utilization. For a general overview of data enrichment patterns, refer to Common streaming data enrichment patterns in Amazon Managed Service for Apache Flink. This post covers how you can implement data enrichment for real-time streaming events with Apache Flink and how you can optimize performance. To compare the performance of the enrichment patterns, we ran performance testing based on synthetic data. The result of this test is useful as a general reference. Itâs important to note that the actual performance for your Flink workload will depend on various and different factors, such as API latency, throughput, size of the event, and cache hit ratio. Unstructured data management and governance using AWS AI/ML and analytics services by Sakti Mishra , Bhavana Chirumamilla , Daniel Bruno , and Sheela Sonone on 25 OCT 2023 in Amazon Athena , Amazon Bedrock , Amazon Comprehend , Amazon DataZone , Amazon Redshift , Amazon Rekognition , Amazon SageMaker , Amazon Simple Storage Service (S3) , Amazon Textract , Amazon Transcribe , Analytics , Architecture , Artificial Intelligence , AWS Big Data , AWS Glue , AWS Lambda , AWS Step Functions , Generative AI , Technical How-to , Thought Leadership Permalink Comments Share In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data. Automated data governance with AWS Glue Data Quality, sensitive data detection, and AWS Lake Formation by Shoukat Ghouse on 10 OCT 2023 in Architecture , AWS Glue , AWS Lake Formation , Thought Leadership Permalink Comments Share Data governance is the process of ensuring the integrity, availability, usability, and security of an organizationâs data. Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. In this post, we showcase how to use AWS Glue with AWS Glue Data Quality, sensitive data detection transforms, and AWS Lake Formation tag-based access control to automate data governance. Non-JSON ingestion using Amazon Kinesis Data Streams, Amazon MSK, and Amazon Redshift Streaming Ingestion by Mazrim Mehrtens and Sindhu Achuthan on 02 OCT 2023 in Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Redshift , AWS Glue , Kinesis Data Streams , Technical How-to , Thought Leadership Permalink Comments Share Organizations are grappling with the ever-expanding spectrum of data formats in todayâs data-driven landscape. From Avroâs binary serialization to the efficient and compact structure of Protobuf, the landscape of data formats has expanded far beyond the traditional realms of CSV and JSON. As organizations strive to derive insights from these diverse data streams, the challenge [âŠ] Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints â Part 2 by Lorenzo Nicora and Francisco Morillo on 14 SEP 2023 in Advanced (300) , Amazon Managed Service for Apache Flink , Analytics , Thought Leadership Permalink Comments Share February 2024: This post was reviewed and updated for accuracy. This post is a continuation of a two-part series. In the first part, we delved into Apache Flinkâs internal mechanisms for checkpointing, in-flight data buffering, and handling backpressure. We covered these concepts in order to understand how buffer debloating and unaligned checkpoints allow us to [âŠ] Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints â Part 1 by Lorenzo Nicora and Francisco Morillo on 14 SEP 2023 in Advanced (300) , Amazon Managed Service for Apache Flink , Analytics , Thought Leadership Permalink Comments Share This post is the first of a two-part series regarding checkpointing mechanisms and in-flight data buffering. In this first part, we explain some of the fundamental Apache Flink internals and cover the buffer debloating feature. In the second part, we focus on unaligned checkpoints. Apache Flink is an open-source distributed engine for stateful processing over [âŠ] Derive operational insights from application logs using Automated Data Analytics on AWS by Aparajithan Vaidyanathan , Hafiz Saadullah , and Rashim Rahman on 16 AUG 2023 in Advanced (300) , Analytics , AWS Big Data , Business Intelligence , Technical How-to , Thought Leadership Permalink Comments Share Automated Data Analytics (ADA) on AWS is an AWS solution that enables you to derive meaningful insights from data in a matter of minutes through a simple and intuitive user interface. ADA offers an AWS-native data analytics platform that is ready to use out of the box by data analysts for a variety of use [âŠ] The art and science of data product portfolio management by Faris Haddad and Maximilian Mayrhofer on 14 AUG 2023 in AWS Big Data , Best Practices , Thought Leadership Permalink Comments Share This post is the first in a series dedicated to the art and science of practical data mesh implementation (for an overview of data mesh, read the original whitepaper The data mesh shift). The series attempts to bridge the gap between the tenets of data mesh and its real-life implementation by deep-diving into the functional [âŠ] â Older posts Newer posts â <a role="button" href="https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?nc1=f_ct&src=footer_signup" rel="noopener noreferrer" target="_blank" data-testid="foot | 2026-01-13T09:29:11 |
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https://aws.amazon.com/blogs/big-data/category/analytics/amazon-athena/#aws-page-content-main | Amazon Athena | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Athena How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share At Twilio, we manage a 20 petabyte-scale Amazon S3 data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift by Shubham Purwar , Nitin Kumar , and Prashanthi Chinthala on 13 OCT 2025 in Amazon Athena , Amazon EMR , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , AWS Glue , Expert (400) , Technical How-to Permalink Comments Share Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including [âŠ] Transform your data to Amazon S3 Tables with Amazon Athena by Pathik Shah and Aritra Gupta on 15 AUG 2025 in Amazon Athena , Amazon S3 Tables , Analytics , Intermediate (200) Permalink Comments Share This post demonstrates how Amazon Athena CREATE TABLE AS SELECT (CTAS) simplifies the data transformation process through a practical example: migrating an existing Parquet dataset into Amazon S3 Tables. Build an analytics pipeline that is resilient to Avro schema changes using Amazon Athena by Mohammad Sabeel and Indira Balakrishnan on 25 JUL 2025 in Amazon Athena , Analytics , AWS Glue , Intermediate (200) Permalink Comments Share This post demonstrates how to build a solution by combining Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue Data Catalog for schema management, and Amazon Athena for one-time querying. Weâll focus specifically on handling Avro-formatted data in partitioned S3 buckets, where schemas can change frequently while providing consistent query capabilities across all data regardless of schema versions. How Stifel built a modern data platform using AWS Glue and an event-driven domain architecture by Amit Maindola and Srinivas Kandi, Hossein Johari, Ahmad Rawashdeh, Lei Meng on 07 JUL 2025 in Advanced (300) , Amazon Athena , Amazon EventBridge , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Experience-Based Acceleration , Technical How-to , Thought Leadership Permalink Comments Share In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products. Introducing managed query results for Amazon Athena by Guy Bachar , Darshit Thakkar , and Sayan Chakraborty on 03 JUN 2025 in Amazon Athena , Analytics , Announcements Permalink Comments Share Weâre thrilled to introduce managed query results, a new Athena feature that automatically stores, secures, and manages the lifecycle of query result data for you at no additional cost. In this post, we demonstrate how to get started with managed query results and, by removing the undifferentiated effort spent on query result management, how Athena helps you get insights from your data in fewer steps than before. Build a secure serverless streaming pipeline with Amazon MSK Serverless, Amazon EMR Serverless and IAM by Shubham Purwar , Nitin Kumar , and Prashanthi Chinthala on 02 JUN 2025 in Amazon Athena , Amazon EMR , Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Analytics , AWS Big Data Permalink Comments Share The post demonstrates a comprehensive, end-to-end solution for processing data from MSK Serverless using an EMR Serverless Spark Streaming job, secured with IAM authentication. Additionally, it demonstrates how to query the processed data using Amazon Athena, providing a seamless and integrated workflow for data processing and analysis. This solution enables near real-time querying of the latest data processed from MSK Serverless and EMR Serverless using Athena, providing instant insights and analytics. How BMW Group built a serverless terabyte-scale data transformation architecture with dbt and Amazon Athena by Philipp Karg , Cizer Pereira , and Selman Ay on 29 APR 2025 in Amazon Athena , Amazon Quick Sight , Analytics , Customer Solutions Permalink Comments Share At the BMW Group, our Cloud Efficiency Analytics (CLEA) team has developed a FinOps solution to optimize costs across over 10,000 cloud accounts This post explores our journey, from the initial challenges to our current architecture, and details the steps we took to achieve a highly efficient, serverless data transformation setup. Amazon SageMaker Lakehouse now supports attribute-based access control by Sandeep Adwankar and Srividya Parthasarathy on 24 APR 2025 in Amazon Athena , Amazon Redshift , Amazon SageMaker Lakehouse , Analytics , Announcements , AWS Glue , AWS Identity and Access Management (IAM) , AWS Lake Formation , Technical How-to Permalink Comments Share Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC. Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode by Aarthi Srinivasan and Parul Saxena on 21 APR 2025 in Amazon Athena , Amazon EMR , AWS Lake Formation , Intermediate (200) Permalink Comments Share In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables. â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases | 2026-01-13T09:29:12 |
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https://aws.amazon.com/blogs/big-data/category/artificial-intelligence/ | Artificial Intelligence | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Artificial Intelligence Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Optimize efficiency with language analyzers using scalable multilingual search in Amazon OpenSearch Service by Sunil Ramachandra , Mingshi Liu , and Sampath Kathirvel on 02 OCT 2025 in Amazon OpenSearch Service , Amazon SageMaker Studio , Technical How-to Permalink Comments Share Organizations manage content across multiple languages as they expand globally. Ecommerce platforms, customer support systems, and knowledge bases require efficient multilingual search capabilities to serve diverse user bases effectively. This unified search approach helps multinational organizations maintain centralized content repositories while making sure users, regardless of their preferred language, can effectively find and access relevant [âŠ] Accelerate your data and AI workflows by connecting to Amazon SageMaker Unified Studio from Visual Studio Code by Lauren Mullennex , Anagha Barve , Anchit Gupta , and Bhargava Varadharajan on 12 SEP 2025 in Amazon SageMaker AI , Amazon SageMaker Unified Studio , Announcements , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we demonstrate how to connect your local VS Code to SageMaker Unified Studio so you can build complete end-to-end data and AI workflows while working in your preferred development environment. The Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control for federated catalogs by Sandeep Adwankar , Aarthi Srinivasan , and Srividya Parthasarathy on 29 AUG 2025 in Amazon SageMaker , Amazon SageMaker Lakehouse , Analytics , Announcements , AWS Big Data , Intermediate (200) Permalink Comments Share We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless. Amazon SageMaker Catalog expands discoverability and governance for Amazon S3 general purpose buckets by Priya Tiruthani , Yuhang Huang , Santhosh Padmanabhan , and Subrat Das on 21 AUG 2025 in Amazon SageMaker , Announcements , Intermediate (200) Permalink Comments Share In July 2025, Amazon SageMaker announced support for Amazon Simple Storage Service (Amazon S3) general purpose buckets and prefixes in Amazon SageMaker Catalog that delivers fine-grained access control and permissions through S3 Access Grants. In this post, we explore how this integration addresses key challenges our customers have shared with us, and how data producers, such as administrators and data engineers, can seamlessly share and govern S3 buckets and prefixes using S3 Access Grants, while making it readily discoverable for data consumers. Guide to adopting Amazon SageMaker Unified Studio from ATPCOâs Journey by Mitesh Patel , Nikki Rouda , Raj Samineni , and Saurabh Rawat on 18 AUG 2025 in Advanced (300) , Amazon SageMaker , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio. Enhance Amazon EMR observability with automated incident mitigation using Amazon Bedrock and Amazon Managed Grafana by Yu-Ting Su on 14 AUG 2025 in Amazon Bedrock , Amazon Bedrock Agents , Amazon Bedrock Knowledge Bases , Amazon EMR , Amazon EventBridge , Amazon Managed Grafana , Amazon Managed Service for Prometheus , Amazon Nova , Analytics , AWS Big Data , AWS Lambda , Technical How-to Permalink Comments Share In this post, we demonstrate how to integrate real-time monitoring with AI-powered remediation suggestions, combining Amazon Managed Grafana for visualization, Amazon Bedrock for intelligent response recommendations, and AWS Systems Manager for automated remediation actions on Amazon Web Services (AWS). Integrate scientific data management and analytics with the next generation of Amazon SageMaker, Part 1 by Nadeem Bulsara , Chaitanya Vejendla , Chris Clark , Nick Furr , Mileidy Giraldo , and Subrat Das on 05 AUG 2025 in Amazon SageMaker , Amazon SageMaker Unified Studio , Announcements , Healthcare , Life Sciences Permalink Comments Share In this blog post, AWS introduces a solution to a common challenge in scientific research â the inefficient management of fragmented scientific data. The post demonstrates how the next generation of Amazon SageMaker, through its Unified Studio and Catalog features, helps scientists streamline their workflow by integrating data management and analytics capabilities. Develop and deploy a generative AI application using Amazon SageMaker Unified Studio by Amit Maindola , Arghya Banerjee , Melody Yang , and Gaurav Parekh on 04 AUG 2025 in Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , Financial Services , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making. Accelerating development with the AWS Data Processing MCP Server and Agent by Shubham Mehta , Arun A K , Liyuan Lin , Mohit Saxena , Vaibhav Naik , Pradeep Patel , and Sarath Krishnan on 22 JUL 2025 in Analytics , Artificial Intelligence Permalink Comments Share Weâre excited to introduce the AWS Data Processing MCP Server, an open-source tool that uses the Model Context Protocol (MCP) to simplify analytics environment setup on AWS. In this post, we explore how the AWS Data Processing MCP Server accelerates analytics solution development and how data engineers can transform raw data into business-ready insights through AI-assisted workflows, significantly reducing development time and complexity. â Older posts @charset "UTF-8";[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4{position:relative;transition:box-shadow .3s ease}[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_3d631df0,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_b27cc003,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_5962fadc:hover{box-shadow:var(--rg-shadow-gray-elevation-1, 1px 1px 20px rgba(0, 0, 0, .1))}[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_3d631df0.rgft_e79955da,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_b27cc003.rgft_e79955da,[data-eb-6a8f3296] 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https://aws.amazon.com/blogs/big-data/category/post-types/thought-leadership/#aws-page-content-main | Thought Leadership | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Thought Leadership How Stifel built a modern data platform using AWS Glue and an event-driven domain architecture by Amit Maindola and Srinivas Kandi, Hossein Johari, Ahmad Rawashdeh, Lei Meng on 07 JUL 2025 in Advanced (300) , Amazon Athena , Amazon EventBridge , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Experience-Based Acceleration , Technical How-to , Thought Leadership Permalink Comments Share In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products. Accelerate analytics and AI innovation with the next generation of Amazon SageMaker by G2 Krishnamoorthy and Rahul Pathak on 13 MAR 2025 in Amazon SageMaker , Amazon SageMaker Unified Studio , Analytics , Artificial Intelligence , Thought Leadership Permalink Comments Share We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started. Cross-account data collaboration with Amazon DataZone and AWS analytical tools by Arun Pradeep Selvaraj , Piyush Mattoo , and Mani Yamaraja on 05 MAR 2025 in Amazon DataZone , Amazon Redshift , AWS Glue , Technical How-to , Thought Leadership Permalink Comments Share In this post, we will cover how you can use Amazon DataZone to facilitate data collaboration between AWS accounts. How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes by Leo Ramsamy , Rada Stanic , and Srinivasan Kuppusamy on 04 DEC 2024 in Amazon Athena , Amazon DataZone , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Quick Sight , Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Customer Solutions , Thought Leadership Permalink Comments Share ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. Run high-availability long-running clusters with Amazon EMR instance fleets by Garima Arora , Ravi Kumar Singh , and Tarun Chanana on 21 NOV 2024 in Amazon EMR , Analytics , Compute , Innovation and Reinvention , Intermediate (200) , News , Technical How-to , Thought Leadership Permalink Comments Share In this post, we demonstrate how to launch a high availability instance fleet cluster using the newly redesigned Amazon EMR console, as well as using an AWS CloudFormation template. We also go over the basic concepts of Hadoop high availability, EMR instance fleets, the benefits and trade-offs of high availability, and best practices for running resilient EMR clusters. How Volkswagen Autoeuropa built a data solution with a robust governance framework, simplifying access to quality data using Amazon DataZone by Dhrubajyoti Mukherjee , Adjoa Taylor , Ajinkya Patil , Martin Mikoleizig , Ravi Kumar , and Weizhou "Wei" Sun on 13 NOV 2024 in Amazon DataZone , Automotive , AWS Glue , Customer Solutions , Enterprise governance and control , Innovation and Reinvention , Intermediate (200) , Technical How-to , Thought Leadership Permalink Comments Share This second post of a two-part series that details how Volkswagen Autoeuropa, a Volkswagen Group plant, together with AWS, built a data solution with a robust governance framework using Amazon DataZone to become a data-driven factory. Part 1 of this series focused on the customer challenges, overall solution architecture and solution features, and how they helped Volkswagen Autoeuropa overcome their challenges. This post dives into the technical details, highlighting the robust data governance framework that enables ease of access to quality data using Amazon DataZone. How Volkswagen Autoeuropa built a data mesh to accelerate digital transformation using Amazon DataZone by Dhrubajyoti Mukherjee , Adjoa Taylor , Martin Mikoleizig , Ravi Kumar , Shameka Almond , and Weizhou "Wei" Sun on 31 OCT 2024 in Amazon DataZone , Automotive , AWS Glue , Customer Solutions , Enterprise governance and control , Innovation and Reinvention , Intermediate (200) , Manufacturing , Technical How-to , Thought Leadership Permalink Comments Share In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance. Optimize your workloads with Amazon Redshift Serverless AI-driven scaling and optimization by Satesh Sonti , Davide Pagano , and Ashish Agrawal on 21 AUG 2024 in Amazon Redshift , Analytics , Artificial Intelligence , Cloud Cost Optimization , Technical How-to , Thought Leadership Permalink Comments Share The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. However, you might need to automatically scale compute resources based on factors like query complexity and data volume to meet price-performance targets, irrespective of query queuing. [âŠ] AWS named a Leader in IDC MarketScape: Worldwide Analytic Stream Processing Software 2024 Vendor Assessment by Anna Montalat on 31 MAY 2024 in Amazon Managed Service for Apache Flink , Analytics , Thought Leadership Permalink Comments Share Weâre thrilled to announce that AWS has been named a Leader in the IDC MarketScape: Worldwide Analytic Stream Processing Software 2024 Vendor Assessment (doc #US51053123, March 2024). We believe this recognition validates the power and performance of Apache Flink for real-time data processing, and how AWS is leading the way to help customers build and [âŠ] Achieve peak performance and boost scalability using multiple Amazon Redshift serverless workgroups and Network Load Balancer by Ricardo Serafim , Amol Gaikaiwari , Harshida Patel , and Urvish Shah on 09 MAY 2024 in Advanced (300) , Amazon Redshift , Technical How-to , Thought Leadership Permalink Comments Share As data analytics use cases grow, factors of scalability and concurrency become crucial for businesses. Your analytic solution architecture should be able to handle large data volumes at high concurrency and without compromising speed, thereby delivering a scalable high-performance analytics environment. Amazon Redshift Serverless provides a fully managed, petabyte-scale, auto scaling cloud data warehouse to [âŠ] â Older posts {"data":{"items":[{"fields":{"footer":"{\n \"createAccountButtonLabel\": \"Create an AWS account\",\n \"createAccountButtonURL\": \"https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?nc1=f_ct&src=footer_signup\",\n \"backToTopText\": \"Back to top\",\n \"eoeText\": \"Amazon is an Equal Opportunity Employer: Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.\",\n \"copyrightText\": \"© 2025, Amazon Web Services, Inc. or its affiliates. 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https://aws.amazon.com/blogs/big-data/accelerate-analytics-and-ai-innovation-with-the-next-generation-of-amazon-sagemaker/#Comments | Accelerate analytics and AI innovation with the next generation of Amazon SageMaker | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Accelerate analytics and AI innovation with the next generation of Amazon SageMaker by G2 Krishnamoorthy and Rahul Pathak on 13 MAR 2025 in Amazon SageMaker , Amazon SageMaker Unified Studio , Analytics , Artificial Intelligence , Thought Leadership Permalink Comments Share At AWS re:Invent 2024, we announced the next generation of Amazon SageMaker , the center for all your data, analytics, and AI. Amazon SageMaker brings together widely adopted AWS machine learning (ML) and analytics capabilities and addresses the challenges of harnessing organizational data for analytics and AI through unified access to tools and data with governance built in. It enables teams to securely find, prepare, and collaborate on data assets and build analytics and AI applications through a single experience, accelerating the path from data to value. At the core of the next generation of Amazon SageMaker is Amazon SageMaker Unified Studio , a single data and AI development environment where you can find and access your organizationâs data and act on it using the best tool for the job across virtually any use case. We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started. Amazon SageMaker Unified Studio SageMaker Unified Studio brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR , AWS Glue , Amazon Athena , Amazon Redshift , Amazon Bedrock , and Amazon SageMaker AI . From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications. Governance features including fine-grained access control are built into SageMaker Unified Studio using Amazon SageMaker Catalog to help you meet enterprise security requirements across your entire data estate. Unified access to your data is provided by Amazon SageMaker Lakehouse , a unified, open, and secure data lakehouse built on Apache Iceberg open standards. Whether your data is stored in Amazon Simple Storage Service (Amazon S3) data lakes, Redshift data warehouses, or third-party and federated data sources, you can access it from one place and use it with Iceberg-compatible engines and tools. In addition, SageMaker Lakehouse now integrates with Amazon S3 Tables , the first cloud object store with native Apache Iceberg support, so you can use SageMaker Lakehouse to create, query, and process S3 Tables efficiently using various analytics engines in SageMaker Unified Studio as well as Iceberg-compatible engines like Apache Spark and PyIceberg. Capabilities from Amazon Bedrock are now generally available in SageMaker Unified Studio, allowing you to rapidly prototype, customize, and share generative AI applications in a governed environment. Users have an intuitive interface to access high-performing foundation models (FMs) in Amazon Bedrock, including the Amazon Nova model series, and the ability to create Agents, Flows, Knowledge Bases, and Guardrails with a few clicks. Amazon Q Developer , the most capable generative AI assistant for software development, can be used within SageMaker Unified Studio to streamline tasks across the data and AI development lifecycle, including code authoring, SQL generation, data discovery, and troubleshooting. A new integrated way of working The general availability of SageMaker Unified Studio represents another meaningful step in our journey to offer our customers a streamlined way to work with their data, whether for analytics or AI. Many of our customers have told us that you are building data-driven applications to guide business decisions, improve agility, and drive innovation, but that these applications are complex to build because they require collaboration across teams and the integration of data and tools. Not only is it time consuming for users to learn multiple development experiences, but because data, code, and other development artifacts are stored separately, it is challenging for users to understand how they interact with each other and to use them cohesively. Configuring and governing access is also a cumbersome manual process. To overcome these hurdles, many organizations are building bespoke integrations between services, tools, and homegrown access management systems. However, what you need is the flexibility to adopt the best services for your use case while empowering your data teams with a unified development experience. âAt Carrier, the next generation of Amazon SageMaker is transforming our enterprise data strategy by streamlining how we build and scale data products. SageMaker Unified Studioâs approach to data discovery, processing, and model development has significantly accelerated our lakehouse implementation. Most impressively, its seamless integration with our existing data catalog and built-in governance controls enables us to democratize data access while maintaining security standards, helping our teams rapidly deliver advanced analytics and AI solutions across the enterprise.â â Justin McDowell, Director Data Platform & Data Engineering, Carrier Millions of organizations trust AWS and utilize our comprehensive set of purpose-built analytics, AI/ML, and generative AI capabilities to power data-driven applications without compromising on performance, scale, or cost. Our goal for the next generation of Amazon SageMaker, including SageMaker Unified Studio, is to make data and AI workers more productive by providing access to all your data and tools in a single development environment. Building from a single data and AI development environment Letâs explore a common business challenge: increasing revenue through better lead generation. Consider an organization implementing an intelligent digital assistant on their website to engage with customersâa process that traditionally requires multiple tools and data sources. With SageMaker Unified Studio, this entire process can now be carried out within a single data and AI development environment. First, the data team uses the generative AI playground within SageMaker Unified Studio to quickly evaluate and select the best model for their customer interactions. They then create a project to house the tools and resources necessary for their use case and use Amazon Bedrock within the project to build and deploy a sophisticated virtual assistant that quickly begins qualifying leads through their website. To identify the most promising opportunities, the team develops a segmentation strategy. The data engineer asks Amazon Q Developer to identify datasets that contain lead data and uses zero-ETL integrations to bring the data into SageMaker Lakehouse. The data analyst then discovers it and creates a comprehensive view of their market. They use the SQL query editor to build out marketing segments, which they then write back to SageMaker Lakehouse, where they are available to other team members. Finally, the data scientist accesses the same dataset, which they use to train and deploy an automated lead scoring model using tools available from SageMaker AI. During the model development phase, they use Amazon Q Developerâs inline code authoring and troubleshooting capabilities to efficiently write error free-code in their JupyterLab notebook. The final model provides sales teams with the highest-value opportunities, which they can visualize in a business intelligence dashboard and take action on immediately. Reducing time-to-value in a unified environment What is remarkable about this example is that entire process happens in one integrated environment. Without SageMaker Unified Studio, the team would have had to work with multiple data sources, tools, and services, spending time learning multiple development environments, creating resources shares, and manually configuring access controls. The data engineer and data analyst would have worked in various data warehouses, data lakes, and analytics tools, the data scientist would have worked in an ML studio and notebook environment, and the application builder in a generative AI tool. Now, theyâre able to build and collaborate with their data and tools available in one experience, dramatically reducing time-to-value. Thatâs why weâre so excited about the next generation of Amazon SageMaker and the general availability of SageMaker Unified Studio. We believe that by putting everything you need for analytics and AI in one place, you can solve complex end-to-end problems more efficiently and get to innovative outcomes faster than ever before. Getting started with SageMaker Unified Studio To learn more, check out the following resources: Amazon SageMaker Amazon SageMaker Unified Studio Amazon SageMaker Lakehouse About the authors G2 Krishnamoorthy is VP of Analytics, leading AWS data lake services, data integration, Amazon OpenSearch Service, and Amazon QuickSight. Prior to his current role, G2 built and ran the Analytics and ML Platform at Facebook/Meta, and built various parts of the SQL Server database, Azure Analytics, and Azure ML at Microsoft. Rahul Pathak is VP of Relational Database Engines, leading Amazon Aurora, Amazon Redshift, and Amazon QLDB. Prior to his current role, he was VP of Analytics at AWS, where he worked across the entire AWS database portfolio. He has co-founded two companies, one focused on digital media analytics and the other on IP-geolocation. 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https://aws.amazon.com/blogs/big-data/medidatas-journey-to-a-modern-lakehouse-architecture-on-aws/ | Medidataâs journey to a modern lakehouse architecture on AWS | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Medidataâs journey to a modern lakehouse architecture on AWS by Mike Araujo , Ashley Chen , Ian Beatty , and Sandeep Adwankar on 26 NOV 2025 in Amazon Kinesis , Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , Case Study , Customer Solutions Permalink Comments Share This post was co-authored by Mike Araujo Principal Engineer at Medidata Solutions. The life sciences industry is transitioning from fragmented, standalone tools towards integrated, platform-based solutions. Medidata , a Dassault SystÚmes company, is building a next-generation data platform that addresses the complex challenges of modern clinical research. In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg , and a modern lakehouse architecture. Challenges with legacy architecture As the Medidata portfolio of data offerings expanded and our strategy evolved to offering an end-to-end clinical data platform experience, the team recognized the need to evolve the data platform architecture. The following diagram shows Medidataâs legacy extract, transform, and load (ETL) architecture. The legacy system was built on scheduled batch jobs, which served Medidata well for a long time, but wasnât designed for a unified view of data across a growing ecosystem. Batch jobs ran at different intervals, requiring scheduling buffers to make sure upstream jobs completed on time. As data volume grew, these schedules became more complex, introducing latency between ingestion and processing. Different consumers pulling from various data services also meant teams were continuously building new pipelines for each delivery stack. As the number of pipelines grew, so did the maintenance burden. More operations meant more potential points of failure and more complex recovery. The observability systems were handling a growing volume of operational data, making root cause analysis more involved. Scaling for increased data volume required coordinated changes across all our systems. Additionally, having data pipelines and copies spread across different technologies and storage systems meant access controls had to be managed in multiple places. Propagating access control changes correctly across all systems added complexity for both consumers and producers. Solution overview With the advent of Clinical Data Studio (Medidataâs unified data management and analytics solution for clinical trials) and Data Connect (Medidataâs data solution for acquiring, transforming, and exchanging electronic health record (EHR) data across healthcare organizations), Medidata introduced a new world of data discovery, analysis, and integration to the life sciences industry powered by open source technologies and hosted on AWS. The following diagram illustrates the solution architecture. Fragmented batch ETL jobs were replaced by real-time Apache Flink streaming pipelines, an open source, distributed engine for stateful processing, and powered by Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed Kubernetes service. The Flink jobs write to Apache Kafka running in Amazon Managed Apache Kafka (Amazon MSK), a streaming data service that manages Kafka infrastructure and operations, before landing in Iceberg tables backed by the AWS Glue Data Catalog , a centralized metadata repository for data assets. From this collection of Iceberg tables, a central, single source of data is now accessible from a variety of consumers without additional downstream processing, alleviating the need for custom pipelines to satisfy the requirements of downstream consumers. Through these fundamental architectural changes, the team at Medidata solved the issues presented by the legacy solution. Data availability and consistency With the introduction of the Flink jobs and Iceberg tables, the team was able to deliver a consistent view of their data across the Medidata data experience. Pipeline latency was reduced from numerous hours and sometimes days to minutes, helping Medidata customers realize a 99% performance gain from the data ingestion to the data analytics layers. Due to Icebergâs interoperability, Medidata users saw the same view of the data regardless of where they viewed that data, minimizing the need for consumer-driven custom pipelines because Iceberg could plug into existing consumers. Maintenance and durability Icebergâs interoperability provided a single copy of the data to satisfy their use cases, so the Medidata team could focus its observation and maintenance efforts on a five-times smaller subset of operations than previously required. Observability was enhanced by tapping into the various metadata components and metrics exposed by Iceberg and the Data Catalog. Quality management transformed from cross-system traces and queries to a single analysis of unified pipelines, with an added benefit of point in time data queries thanks to the Iceberg snapshot feature . Data volume increases are handled with out-of-box scaling supported by the entire infrastructure stack and AWS Glue Iceberg optimization features that include compaction , snapshot retention , and orphan file deletion , which provide a set-and-forget experience for solving a number of common Iceberg frustrations, such as the small file problem, orphan file retention, and query performance. Security With Iceberg at the center of its solution architecture, the Medidata team no longer had to spend the time building custom access control layers with enhanced security features at each data integration point. Iceberg on AWS centralizes the authorization layer using familiar systems such as AWS Identity and Access Management (IAM), providing a single and durable control for data access. The data also stays entirely within the Medidata virtual private cloud (VPC), further reducing the opportunity for unintended disclosures. Conclusion In this post, we demonstrated how legacy universe of consumer-driven custom ETL pipelines can be replaced with a scalable, high-performant streaming lakehouses. By putting Iceberg on AWS at the center of data operations, you can have a single source of data for your consumers. To learn more about Iceberg on AWS, refer to Optimizing Iceberg tables and Using Apache Iceberg on AWS . About the authors Mike Araujo Mike is a Principal Engineer at Medidata Solutions, working on building a next generation data and AI platform for clinical data and trials. By using the power of open source technologies such as Apache Kafka, Apache Flink, and Apache Iceberg, Mike and his team have enabled the delivery of billions of clinical events and data transformations in near real time to downstream consumers, applications, and AI agents. His core skills focus on architecting and building big data and ETL solutions at scale as well as their integration in agentic workflows. Sandeep Adwankar Sandeep is a Senior Product Manager at AWS, who has driven feature launches across Amazon SageMaker, AWS Glue, and AWS Lake Formation. He has led initiatives in Amazon S3 Tables analytics, Iceberg compaction strategies, and AWS Glue Iceberg optimizations. His recent work focuses on generative AI and autonomous systems, including the AWS Glue Data Catalog model context protocol and Amazon Bedrock structured knowledge bases. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that accelerate their business outcomes. Ian Beatty Ian is a Technical Account Manager at AWS, where he specializes in supporting independent software vendor (ISV) customers in the healthcare and life sciences (HCLS) and financial services industry (FSI) sectors. Based in the Rochester, NY area, Ian helps ISV customers navigate their cloud journey by maintaining resilient and optimized workloads on AWS. With over a decade of experience building on AWS since 2014, he brings deep technical expertise from his previous roles as an AWS Architect and DevSecOps team lead for SaaS ISVs before joining AWS more than 3 years ago. Ashley Chen Ashley is a Solutions Architect at AWS based in Washington D.C. She supports independent software vendor (ISV) customers in the healthcare and life sciences industries, focusing on customer enablement, generative AI applications, and container workloads. Loading comments⊠Resources Amazon Athena Amazon EMR Amazon Kinesis Amazon MSK Amazon QuickSight Amazon Redshift AWS Glue Follow Twitter Facebook LinkedIn Twitch Email Updates Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? 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https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/#dirlist | src/ src/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src File Rev. Age Author Last log entry Parent Directory bin/ distrib/ etc/ games/ gnu/ include/ kerberosIV/ kerberosV/ lib/ libexec/ lkm/ regress/ sbin/ share/ sys/ usr.bin/ usr.sbin/ .gitignore 1.3 2 years tobhe Ignore CVS directories for easier git + CVS coexistence. ok bluhm@ stsp@ Makefile 1.136 5 years deraadt minor cranking of libraries exposes a problem, details too long to explain. Run ... Makefile.cross 1.112 2 months miod Prepare for gcc 3 leaving the building, COMPILER_VERSION can no longer get set t... Show only files with tag: All tags / default branch OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN HEAD Module path or alias: | 2026-01-13T09:29:12 |
https://twitter.com/intent/tweet/?text=AWS%20analytics%20at%20re%3AInvent%202025%3A%20Unifying%20Data%2C%20AI%2C%20and%20governance%20at%20scale&via=awscloud&url=https://aws.amazon.com/blogs/big-data/aws-analytics-at-reinvent-2025-unifying-data-ai-and-governance-at-scale/ | JavaScript is not available. Weâve detected that JavaScript is disabled in this browser. Please enable JavaScript or switch to a supported browser to continue using x.com. You can see a list of supported browsers in our Help Center. Help Center Terms of Service Privacy Policy Cookie Policy Imprint Ads info © 2026 X Corp. Something went wrong, but donât fret â letâs give it another shot. Try again Some privacy related extensions may cause issues on x.com. Please disable them and try again. | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/analytics/amazon-redshift-analytics/page/2/ | Amazon Redshift | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Redshift Bridging data silos: cross-bounded context querying with Vanguardâs Operational Read-only Data Store (ORDS) using Amazon Redshift by Naresh Rajaram , Malav Shah , Priyadharshini Selvaraj , and Timothy Dickens on 06 OCT 2025 in Amazon Redshift , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share At Vanguard, we faced significant challenges with our legacy mainframe system that limited our ability to deliver modern, personalized customer experiences. Our centralized database architecture created performance bottlenecks and made it difficult to scale services independently for our millions of personal and institutional investors. In this post, we show you how we modernized our data architecture using Amazon Redshift as our Operational Read-only Data Store (ORDS). Accelerating SQL analytics with Amazon Redshift MCP server by Ramkumar Nottath and Rohit Vashishtha on 23 SEP 2025 in Advanced (300) , Amazon Redshift , Technical How-to Permalink Comments Share In this post, we walk through setting up the Amazon Redshift MCP server and demonstrate how a data analyst can efficiently explore Redshift data warehouses and perform data analysis using natural language queries. Integrate Tableau and PingFederate with Amazon Redshift using AWS IAM Identity Center by Rohit Vashishtha , Jared Warren , Jason Veinot , and Maneesh Sharma on 18 SEP 2025 in Advanced (300) , Amazon Redshift , Technical How-to Permalink Comments Share In this post, we outline a comprehensive guide for setting up single sign-on from Tableau desktop to Amazon Redshift using integration with IAM Identity Center and PingFederate as the identity provider (IdP) with an LDAP based data store, AWS Directory Service for Microsoft Active Directory. Modernize Amazon Redshift authentication by migrating user management to AWS IAM Identity Center by Ziad Wali , Maneesh Sharma , Satesh Sonti , and Sumanth Punyamurthula on 27 AUG 2025 in Advanced (300) , Amazon Redshift , Analytics , AWS IAM Identity Center , Technical How-to Permalink Comments Share Amazon Redshift is a powerful cloud-based data warehouse that organizations can use to analyze both structured and semi-structured data through advanced SQL queries. As a fully managed service, it provides high performance and scalability while allowing secure access to the data stored in the data warehouse. Organizations worldwide rely on Amazon Redshift to handle massive [âŠ] Zero-ETL: How AWS is tackling data integration challenges by Nikki Rouda on 26 AUG 2025 in Amazon Redshift , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , AWS Glue Permalink Comments Share In this blog post, we show you how Amazon Web Services (AWS) is simplifying data integration with zero-ETL while realizing performance benefits and cost optimizations. As organizations gather data for analytics and AI, they are increasingly finding themselves caught in a complex web of extract, transform, and load (ETL) pipelinesâthe traditional backbone of data integration. [âŠ] Best practices for migrating Teradata BTEQ scripts to Amazon Redshift RSQL by Ankur Bhanawat and Raj Patel on 25 AUG 2025 in Amazon Redshift , Best Practices , Migration Permalink Comments Share When migrating from Teradata BTEQ (Basic Teradata Query) to Amazon Redshift RSQL, following established best practices helps ensure maintainable, efficient, and reliable code. While the AWS Schema Conversion Tool (AWS SCT) automatically handles the basic conversion of BTEQ scripts to RSQL, it primarily focuses on SQL syntax translation and basic script conversion. However, to achieve [âŠ] Amazon Redshift Serverless at 4 RPUs: High-value analytics at low cost by Ricardo Serafim , Ashish Agrawal , and Andre Hass on 22 AUG 2025 in Amazon Redshift , Analytics , Announcements , Serverless Permalink Comments Share Amazon Redshift Serverless now supports 4 RPU configurations, helping you get started with a lower base capacity that runs scalable analytics workloads beginning at $1.50 per hour. In this post, we examine how this new sizing option makes Redshift Serverless accessible to smaller organizations while providing enterprises with cost-effective environments for development, testing, and variable workloads. Build data pipelines with dbt in Amazon Redshift using Amazon MWAA and Cosmos by Cindy Li , Akhil B , Harshana Nanayakkara , and Joao Palma on 13 AUG 2025 in Advanced (300) , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Redshift , Technical How-to Permalink Comments Share In this post, we explore a streamlined, configuration-driven approach to orchestrate dbt Core jobs using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Cosmos, an open source package. These jobs run transformations on Amazon Redshift. With this setup, teams can collaborate effectively while maintaining data quality, operational efficiency, and observability. Near real-time streaming analytics on protobuf with Amazon Redshift by Konstantinos Tzouvanas , John Mousa , Marios Parthenios , and Pavlos Kaimakis on 04 AUG 2025 in Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Redshift , AWS Lambda , Technical How-to Permalink Comments Share In this post, we explore an end-to-end analytics workload for streaming protobuf data, by showcasing how to handle these data streams with Amazon Redshift Streaming Ingestion, deserializing and processing them using AWS Lambda functions, so that the incoming streams are immediately available for querying and analytical processing on Amazon Redshift. Amazon Redshift out-of-the-box performance innovations for data lake queries by Martin Milenkoski , Jonathan Katz , and Kalaiselvi Kamaraj on 31 JUL 2025 in Amazon Redshift , Expert (400) Permalink Comments Share In this post, we first briefly review how planner statistics are collected and what impact they have on queries. Then, we discuss Amazon Redshift features that deliver optimal plans on Iceberg tables and Parquet data even with the lack of statistics. Finally, we review some example queries that now execute faster because of these latest Amazon Redshift innovations. â Older posts Newer posts â Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases Resources Getting Started Training AWS Trust Center AWS Solutions Library Architecture Center Product and Technical FAQs Analyst Reports AWS Partners Developers Builder Center SDKs & Tools | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/storage/amazon-simple-storage-services-s3/#aws-page-content-main | Amazon Simple Storage Service (S3) | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Simple Storage Service (S3) Medidataâs journey to a modern lakehouse architecture on AWS by Mike Araujo , Ashley Chen , Ian Beatty , and Sandeep Adwankar on 26 NOV 2025 in Amazon Kinesis , Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , Case Study , Customer Solutions Permalink Comments Share In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture. Accelerate data lake operations with Apache Iceberg V3 deletion vectors and row lineage by Ron Ortloff on 26 NOV 2025 in Amazon EMR , Amazon SageMaker , Amazon Simple Storage Service (S3) , Announcements , AWS Glue , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services. Amazon SageMaker introduces Amazon S3 based shared storage for enhanced project collaboration by Hari Ramesh , Anagha Barve , Anchit Gupta , Saurabh Bhutyani , and Zach Mitchell on 16 SEP 2025 in Amazon SageMaker Unified Studio , Amazon Simple Storage Service (S3) , Announcements , Intermediate (200) , Technical How-to Permalink Comments Share AWS recently announced that Amazon SageMaker now offers Amazon Simple Storage Service (Amazon S3) based shared storage as the default project file storage option for new Amazon SageMaker Unified Studio projects. This feature addresses the deprecation of AWS CodeCommit while providing teams with a straightforward and consistent way to collaborate on project files across the [âŠ] Break down data silos and seamlessly query Iceberg tables in Amazon SageMaker from Snowflake by Nidhi Gupta and Andries Engelbrecht on 15 SEP 2025 in Advanced (300) , Amazon SageMaker Lakehouse , Amazon Simple Storage Service (S3) , AWS Glue , AWS Lake Formation , Partner solutions , S3 Select , Technical How-to Permalink Comments Share This blog post discusses how to create a seamless integration between Amazon SageMaker Lakehouse and Snowflake for modern data analytics. It specifically demonstrates how organizations can enable Snowflake to access tables in AWS Glue Data Catalog (stored in S3 buckets) through SageMaker Lakehouse Iceberg REST Catalog, with security managed by AWS Lake Formation. The post provides a detailed technical walkthrough of implementing this integration, including creating IAM roles and policies, configuring Lake Formation access controls, setting up catalog integration in Snowflake, and managing data access permissions. While four different patterns exist for accessing Iceberg tables from Snowflake, the blog focuses on the first pattern using catalog integration with SigV4 authentication and Lake Formation credential vending. Optimizing vector search using Amazon S3 Vectors and Amazon OpenSearch Service by Sohaib Katariwala , Bobby Mohammed , Sorabh Hamirwasia , Mark Twomey , and Pallavi Priyadarshini on 21 JUL 2025 in Advanced (300) , Amazon OpenSearch Service , Amazon Simple Storage Service (S3) , Analytics , Artificial Intelligence , Launch , Storage , Technical How-to Permalink Comments Share We now have a public preview of two integrations between Amazon Simple Storage Service (Amazon S3) Vectors and Amazon OpenSearch Service that give you more flexibility in how you store and search vector embeddings. In this post, we walk through this seamless integration, providing you with flexible options for vector search implementation. Compaction support for Avro and ORC file formats in Apache Iceberg tables in Amazon S3 by Angel Conde Manjon , Diego Colombatto , and Sandeep Adwankar on 15 JUL 2025 in Amazon Simple Storage Service (S3) , Announcements Permalink Comments Share In this post, we explore how Amazon S3 Tables has expanded its automatic compaction capabilities to include Avro and ORC file formats for Apache Iceberg tables, alongside the previously supported Parquet format. Through performance testing with over 20 billion events, the capability demonstrates significant query performance improvements ranging from 12% to 40% when using compacted tables compared to non-compacted tables across different file formats. How Stifel built a modern data platform using AWS Glue and an event-driven domain architecture by Amit Maindola and Srinivas Kandi, Hossein Johari, Ahmad Rawashdeh, Lei Meng on 07 JUL 2025 in Advanced (300) , Amazon Athena , Amazon EventBridge , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Experience-Based Acceleration , Technical How-to , Thought Leadership Permalink Comments Share In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products. Stream data from Amazon MSK to Apache Iceberg tables in Amazon S3 and Amazon S3 Tables using Amazon Data Firehose by Pratik Patel , Amar Surjit , and Priyanka Chaudhary on 20 JUN 2025 in Amazon Data Firehose , Amazon S3 Tables , Amazon Simple Storage Service (S3) , Architecture , AWS Big Data Permalink Comments Share In this post, we walk through two solutions that demonstrate how to stream data from your Amazon MSK provisioned cluster to Iceberg-based data lakes in Amazon S3 using Amazon Data Firehose. Reduce time to access your transactional data for analytical processing using the power of Amazon SageMaker Lakehouse and zero-ETL by Avijit Goswami , Saman Irfan , and Sudarshan Narasimhan on 16 JUN 2025 in Amazon Redshift , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Amazon Simple Storage Service (S3) , Analytics , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). With this integration, you can now seamlessly onboard the changed data from OLTP systems to a unified lakehouse and expose the same to analytical applications for consumptions using Apache Iceberg APIs from new SageMaker Unified Studio. Simplify enterprise data access using the Amazon Redshift integration with Amazon S3 Access Grants by Maneesh Sharma , Praveen Kumar Ramakrishnan , Laura Reith , and Yanzhu Ji on 16 MAY 2025 in Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , AWS IAM Identity Center Permalink Comments Share In this post, we show how to grant Amazon S3 permissions to IAM Identity Center users and groups using S3 Access Grants. We also test the integration using an IAM Identity Center federated user to unload data from Amazon Redshift to Amazon S3 and load data from Amazon S3 to Amazon Redshift. â Older posts {"data":{"items":[{"fields":{"footer":"{\n \"createAccountButtonLabel\": \"Create an AWS account\",\n \"createAccountButtonURL\": \"https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?nc1=f_ct&src=footer_signup\",\n \"backToTopText\": \"Back to top\",\n \"eoeText\": \"Amazon is an Equal Opportunity Employer: Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.\",\n \"copyrightText\": \"© 2025, Amazon Web Services, Inc. or its affiliates. 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https://aws.amazon.com/blogs/big-data/how-twilio-built-a-multi-engine-query-platform-using-amazon-athena-and-open-source-presto/#Comments | How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share Twilio is a customer engagement platform that powers real-time, personalized customer experiences for leading brands through APIs that democratize communications channels like voice, text, chat, and video. At Twilio, we manage a 20 petabyte-scale Amazon Simple Storage Service (Amazon S3) data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly, and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. A growing need for a multi-engine platform Our data platform has been built on Presto since its inception, but over the years as we expanded to support multiple business lines and diverse use cases, we began to encounter challenges related to scalability, operational overhead, and cost management. Maintaining the platform through frequent version upgrades also became difficult. These upgrades required significant time to evaluate backwards compatibility, integrate with our existing data ecosystem, and determine optimal configurations across releases. The administrative burden of upgrades and our commitment to minimizing user disruption caused our Presto version to fall behind. This prevented us from accessing the latest features and optimizations available in later releases. The adoption of Apache Hudi for our transaction-dependent critical workloads created a new requirement which our existing Presto deployment version couldnât support. We needed an up-to-date Presto or Trino compatible service to accommodate these use cases while still reducing the operational overhead of maintaining our own query infrastructure. Building a comprehensive data platform required us to balance multiple competing requirements and business constraints. We needed a solution that could support diverse workload types, from interactive analytics to ETL batch processing, while providing the flexibility to optimize compute resources based on specific use cases. We also wanted to improve upon cost management and attribution in our shared multi-tenanted query platform. Additionally, we needed to ensure that adopting any new technology did not cause any disruption to our users and maintained backward compatibility with existing systems during the transition period. Selecting Amazon Athena as our modern analytics engine Our users relied on SQL for interactive analysis, and we wanted to preserve this experience and make use of our existing jobs and application code. This meant we needed a Presto-compatible analytics service to modernize our data platform. Amazon Athena is a serverless interactive query service built on Presto and Trino that allows you to run queries using a familiar ANSI SQL interface. Athena appealed to us due to its compatibility with open-source Trino and its seamless upgrade experience. Athena helps to ease the burden of managing a large-scale query infrastructure, and with provisioned capacity , offers predictable and scalable pricing for our largest query workloads. Athenaâs workgroups provided the query and cost management capabilities we needed to efficiently support diverse teams and workload patterns with minimal overhead. The ability to blend on-demand and dedicated serverless capacity models allows us to optimize workload distribution for our requirements, achieving the flexibility and scalability needed in a managed query environment. To address latency-sensitive and predictive query workloads, we adopted provisioned capacity for its serverless capacity guarantee and workload concurrency control features. For queries that may be ad-hoc and more flexible in scheduling, we opted to use the cost-efficient multi-tenant on-demand model, which optimizes resource utilization through shared infrastructure. In parallel to migrating workloads to Athena, we also needed a way to support legacy workloads that use custom implementations of Presto features. This requirement drove us to abstract the underlying implementation, allowing us to present users with a unified interface. This would give us the flexibility key to future proof our infrastructure and use the most appropriate compute for the workload and use case. The birth of Odin The following diagram shows Twilioâs multi-engine query platform that incorporates both Amazon Athena and open-source Presto. High Level Architecture of Odinâs Query Engines Odin is a Presto-based gateway built on Zuul , an open-source L7 application gateway developed by Netflix. Zuul had already demonstrated its scalability at Twilio, having been successfully adopted by other internal teams. Since end users primarily connect to the platform via a JDBC connector using the Presto Driver (which operates through HTTP calls), Zuulâs specialization in HTTP call management made it an ideal technical choice for our needs. Odin functions as a central hub for query processing, employing a pluggable design that accommodates various query frameworks for maximum extensibility and flexibility. To interact with the Odin platform users are initially directed to an Amazon Application Load Balancer that sits in front of the Odin instances running on Amazon EC2. The Odin instances handle the authentication, routing, and entire query workflow throughout the queryâs lifetime. Amazon ElastiCache for Redis handles the query tracking for Athena and Amazon DynamoDB is responsible for the maintaining the query history. Both query engines, Amazon Athena and the Presto clusters running on Amazon EC2,are supported by the AWS Glue Data Catalog as the metastore repository and query data from our Amazon S3-based data lake. Routing queries to multiple engines We had a variety of use cases that were being served by this query platform and therefore we opted to use Amazon Athena as our primary query engine while continuing to route certain legacy workloads to our Presto clusters. Prior to our architectural redesign, we encountered operational challenges due to our end users being tightly bound to specific Presto clusters which led to inevitable disruptions during maintenance windows. Additionally, users frequently overloaded individual clusters with diverse workloads ranging from lightweight ad-hoc analytics to complex data warehousing queries and resource-intensive ETL processes. This prompted us to implement a more sophisticated routing solution, one that was use case focused and not tightly bound to the specific underlying compute. To enable routing across multiple query engines within the same platform, we developed a query hint mechanism that allows users to specify their intended use case. Users append this hint to the JDBC string via the X-Presto-Extra-Credential header , which Odinâs logical routing layer then evaluates alongside multiple factors including user identity, query origin, and fallback planning. The system also assesses whether the target resource has sufficient capacity, if not, it reroutes the query to an alternative resource with available capacity. While users provide initial context through their hints, Odin makes the final routing decisions intelligently on the server side. This approach balances user input with centralized orchestration, ensuring consistent performance and resource availability. For example, say a user might specify the following connection string when connecting to the Odin platform from a Tableau client: jdbc:presto://odin.twilio.com:443/hive?SSL=true&extraCredentials=routing:athena The connection string uses the extraCredentials header to signal execution on Athena, where Odin validates query submission details, including the submitting user and tool, before determining the appropriate Athena workgroup for initial routing. Since this Tableau data source and user qualify as âcritical queries,â the system routes them to a workgroup backed by capacity reservations. However, if that workgroup has too many pending queries in the execution queue, Odinâs routing logic automatically redirects to alternative workgroups with greater available resources. When necessary, queries may ultimately route to workgroups running on on-demand capacity. Through this fallback logic, Odin provides built-in load balancing at the routing layer, ensuring optimal utilization across the underlying compute infrastructure. Here is an example workflow of how our queries are routed to Athena workgroups: Once a query has been submitted to a workgroup for execution, Odin will also log the routing decision in our tracking system based on Amazon ElastiCache for Redis so that Odinâs routing logic can maintain real-time awareness of queue depths across all Athena workgroups. Additionally, Odin uses Amazon EventBridge to integrate with Amazon Athena to keep track of a query state change and create event-based workflows. Our Redis-based query tracking system effectively handles edge cases, such as when a JDBC client terminates mid-query. Even during such unexpected interruptions, the platform consistently maintains and updates the accurate state of the query. Query history Following successful query routing to either an Athena workgroup or one of our open-source Presto clusters, Odin persists the query identifier and destination endpoint in a query history table in DynamoDB. This design utilizes a RESTful architecture where initial query submissions operate as POST requests, while subsequent status checks function as GET requests that utilize DynamoDB as the authoritative lookup mechanism to locate and poll the appropriate execution engine. By centralizing query execution records in DynamoDB rather than maintaining state on individual servers, weâve created a truly stateless system where incoming requests can be handled by any Amazon EC2 instance hosting our Odin web service. Lessons learned The transition from open-source Presto to Athena required some adaptation time, due to subtle differences in how these query engines operate. Since our Odin framework was built on the Presto driver, we needed to modify our processing approach to ensure compatibility between both systems. As we began to adopt Athena for more use cases, we noticed a difference in the record counts between Athena and the original Presto queries. We discovered this was due to open-source Presto returning results with every page containing a header column, whereas Athena results only contain the header column on the first page and subsequent pages containing records only. This difference meant that for a 60-page result set, Athena would return 59 fewer rows than open-source Presto. Once we identified this pagination behavior, we optimized Odinâs result handling logic to properly interpret and process Athenaâs format, so that queries would return accurate results. Due to the nature of using the Odin platform, most of our interactions with the Athena service are API driven so we make use of the ResultSet object with the GetQueryResults API to retrieve query execution data. Using this mechanism, the API returns the data as all VARCHAR data type, even for complex types such as row, map, or array. This created a challenge because Odin uses the Presto driver for query parsing, resulting in a type mismatch between the expected formats and actual returned data. To address this, we implemented a translation layer within the Odin framework that converts all data types to VARCHAR and handles any downstream implications of this conversion internally. These technical adjustments, while initially challenging, highlighted the importance of carefully managing the subtle differences between different query execution engines when building a unified data platform. Scale of Odin and looking ahead The Odin platform serves over 1,500 users who execute approximately 80,000 queries daily, totaling 2.5 million queries per month. Odin also powers more than 5,000 Business Intelligence (BI) reports and dashboards for Tableau and Looker. The queries are executed across our multi-engine landscape of more than 30 workgroups in Athena based on both provisioned capacity and on-demand workgroups and 4 Presto clusters on running on EC2 instances with Auto Scaling enabled that run on average 180 instances each. As Twilio continues to experience rapid growth, our Odin platform has enabled us to mature our technology stacks by both upgrading existing compute resources and integrating new technologies. We can do all this without disrupting the experience for our end users. While Odin serves as our foundation, weâre excited to continue to expand this pluggable infrastructure. Our roadmap includes migrating our self-managed open-source Presto implementation to EMR Trino, introducing Apache Spark as a compute engine via Amazon EMR Serverless or AWS Glue jobs, and integrating generative AI capabilities to intelligently route queries across Odinâs various compute options. Conclusion In this post, weâve shared how we built Odin, our unified multi-engine query platform. By combining AWS services like Amazon Athena, Amazon ElastiCache for Redis, and Amazon DynamoDB with our open-source technology stack, we created a transparent abstraction layer for users. This integration has resulted in a highly available and resilient platform environment that serves our query processing needs. By embracing this multi-engine approach, not only did we solve our query infrastructure challenges but we also established a flexible foundation that will continue to evolve with our data needs, ensuring we can deliver powerful insights at scale regardless of how technology trends shift in the future. To learn more and get started using Amazon Athena, please see the Athena User Guide . About the authors Aakash Pradeep Aakash is a Senior Software Engineer at Adobe with over 15 years of experience across ingestion, compute, storage, and query platforms. Previously, at Twilio, he worked extensively on developing the Odin platform to serve his customers query infrastructure needs. Aakash is a PrestoCon speaker, holds multiple patents in real-time analytics, and is passionate about building high-performance distributed systems. Venkatram Bondugula Venkatram is a seasoned backend engineer with over a decade of experience specializing in the design and development of scalable data platforms for big data and distributed systems. With a strong background in backend architecture and data engineering, he has built and optimized high-performance systems that power data-driven decision-making at scale. Amber Runnels Amber is a Senior Analytics Specialist Solutions Architect at AWS specializing in big data and distributed systems. She helps customers optimize workloads in the AWS data ecosystem to achieve a scalable, performant, and cost-effective architecture. Aside from technology, she is passionate about exploring the many places and cultures this world has to offer, reading novels, and building terrariums. TAGS: Presto Loading comments⊠Resources Amazon Athena Amazon EMR Amazon Kinesis Amazon MSK Amazon QuickSight Amazon Redshift AWS Glue Follow Twitter Facebook LinkedIn Twitch Email Updates @charset "UTF-8";[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4{position:relative;transition:box-shadow .3s ease}[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_3d631df0,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_b27cc003,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_5962fadc:hover{box-shadow:var(--rg-shadow-gray-elevation-1, 1px 1px 20px rgba(0, 0, 0, .1))}[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_3d631df0.rgft_e79955da,[data-eb-6a8f3296] .rgft_9e423fbb.rgft_1b2a14d4:not(:disabled,.rgft_3ef5a62a).rgft_b27cc003.rgft_e79955da,[data-eb-6a8f3296] 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https://aws.amazon.com/blogs/big-data/category/storage/amazon-simple-storage-services-s3/page/2/ | Amazon Simple Storage Service (S3) | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Simple Storage Service (S3) Accelerate your analytics with Amazon S3 Tables and Amazon SageMaker Lakehouse by Sandeep Adwankar , Aditya Kalyanakrishnan , and Srividya Parthasarathy on 17 APR 2025 in Amazon Athena , Amazon Redshift , Amazon SageMaker Lakehouse , Amazon SageMaker Studio , Amazon Simple Storage Service (S3) , Analytics , Announcements , AWS Glue , AWS Identity and Access Management (IAM) , AWS Lake Formation Permalink Comments Share Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables. Build unified pipelines spanning multiple AWS accounts and Regions with Amazon MWAA by Anubhav Gupta , Anusha Pininti , Geetha Penmatsa , Sriharsh Adari , and Suba Palanisamy on 10 APR 2025 in Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Simple Storage Service (S3) , AWS Glue , Technical How-to Permalink Comments Share In this blog post, we demonstrate how to use Amazon MWAA for centralized orchestration, while distributing data processing and machine learning tasks across different AWS accounts and Regions for optimal performance and compliance. Using Amazon S3 Tables with Amazon Redshift to query Apache Iceberg tables by Jonathan Katz and Satesh Sonti on 24 MAR 2025 in Amazon Redshift , Amazon SageMaker Lakehouse , Amazon Simple Storage Service (S3) Permalink Comments Share In this post, we demonstrate how to get started with S3 Tables and Amazon Redshift Serverless for querying data in Iceberg tables. We show how to set up S3 Tables, load data, register them in the unified data lake catalog, set up basic access controls in SageMaker Lakehouse through AWS Lake Formation, and query the data using Amazon Redshift. How Open Universities Australia modernized their data platform and significantly reduced their ETL costs with AWS Cloud Development Kit and AWS Step Functions by Michael Davies and Emma Arrigo on 30 JAN 2025 in Amazon AppFlow , Amazon EventBridge , Amazon Redshift , Amazon Redshift , Amazon Simple Storage Service (S3) , Asia Pacific , AWS Glue , AWS Lambda , AWS Serverless Application Model , AWS Step Functions , Customer Solutions , Education , Higher education Permalink Comments Share At Open Universities Australia (OUA), we empower students to explore a vast array of degrees from renowned Australian universities, all delivered through online learning. In this post, we show you how we used AWS services to replace our existing third-party ETL tool, improving the teamâs productivity and producing a significant reduction in our ETL operational costs. Hybrid big data analytics with Amazon EMR on AWS Outposts by Shoukat Ghouse and Fernando Galves on 29 JAN 2025 in Amazon EMR , Amazon Simple Storage Service (S3) , AWS Glue , AWS Lake Formation , AWS Outposts rack Permalink Comments Share In this post, we dive into the transformative features of EMR on Outposts, showcasing its flexibility as a native hybrid data analytics service that allows seamless data access and processing both on premises and in the cloud. How MuleSoft achieved cloud excellence through an event-driven Amazon Redshift lakehouse architecture by Sean Zou , Terry Quan , Audrey Yuan , Avijit Goswami , and Rueben Jimenez on 28 JAN 2025 in Amazon EventBridge , Amazon Redshift , Amazon Simple Storage Service (S3) , AWS Glue , AWS Trusted Advisor , AWS Well-Architected , Customer Solutions Permalink Comments Share In our previous thought leadership blog post Why a Cloud Operating Model we defined a COE Framework and showed why MuleSoft implemented it and the benefits they received from it. In this post, weâll dive into the technical implementation describing how MuleSoft used Amazon EventBridge, Amazon Redshift, Amazon Redshift Spectrum, Amazon S3, & AWS Glue to implement it. Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction by Navnit Shukla , Angel Conde Manjon , Amit Singh , and Sandeep Adwankar on 19 DEC 2024 in Advanced (300) , Amazon Simple Storage Service (S3) , Analytics , Announcements , AWS Big Data , AWS Glue , AWS Lake Formation Permalink Comments Share In this post, we explore new features of the AWS Glue Data Catalog, which now supports improved automatic compaction of Iceberg tables for streaming data, making it straightforward for you to keep your transactional data lakes consistently performant. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt by Nancy Wu , Xu Feng , and Da Xu on 12 DEC 2024 in Amazon Athena , Amazon DataZone , Amazon Neptune , Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , AWS Lambda , AWS Step Functions , Technical How-to Permalink Comments Share In this post, we use dbt for data modeling on both Amazon Athena and Amazon Redshift. dbt on Athena supports real-time queries, while dbt on Amazon Redshift handles complex queries, unifying the development language and significantly reducing the technical learning curve. Using a single dbt modeling language not only simplifies the development process but also automatically generates consistent data lineage information. This approach offers robust adaptability, easily accommodating changes in data structures. Read and write S3 Iceberg table using AWS Glue Iceberg Rest Catalog from Open Source Apache Spark by Raj Ramasubbu , Pratik Das , and Srividya Parthasarathy on 04 DEC 2024 in Amazon Simple Storage Service (S3) , Analytics , AWS Glue , AWS Lake Formation Permalink Comments Share In this post, we will explore how to harness the power of Open source Apache Spark and configure a third-party engine to work with AWS Glue Iceberg REST Catalog. The post will include details on how to perform read/write data operations against Amazon S3 tables with AWS Lake Formation managing metadata and underlying data access using temporary credential vending. How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes by Leo Ramsamy , Rada Stanic , and Srinivasan Kuppusamy on 04 DEC 2024 in Amazon Athena , Amazon DataZone , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Quick Sight , Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Customer Solutions , Thought Leadership Permalink Comments Share ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. â Older posts Newer posts â {"data":{"items":[{"fields":{"footer":"{\n \"createAccountButtonLabel\": \"Create an AWS account\",\n \"createAccountButtonURL\": \"https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?nc1=f_ct&src=footer_signup\",\n \"backToTopText\": \"Back to top\",\n \"eoeText\": \"Amazon is an Equal Opportunity Employer: Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.\",\n \"copyrightText\": \"© 2025, Amazon Web Services, Inc. or its affiliates. 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https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/#dirlist | src/ src/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src File Rev. Age Author Last log entry Parent Directory bin/ distrib/ etc/ games/ gnu/ include/ kerberosIV/ kerberosV/ lib/ libexec/ lkm/ regress/ sbin/ share/ sys/ usr.bin/ usr.sbin/ .gitignore 1.3 2 years tobhe Ignore CVS directories for easier git + CVS coexistence. ok bluhm@ stsp@ Makefile 1.136 5 years deraadt minor cranking of libraries exposes a problem, details too long to explain. Run ... Makefile.cross 1.112 2 months miod Prepare for gcc 3 leaving the building, COMPILER_VERSION can no longer get set t... Show only files with tag: All tags / default branch OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN HEAD Module path or alias: | 2026-01-13T09:29:12 |
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https://aws.amazon.com/blogs/big-data/accelerate-analytics-and-ai-innovation-with-the-next-generation-of-amazon-sagemaker/ | Accelerate analytics and AI innovation with the next generation of Amazon SageMaker | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Accelerate analytics and AI innovation with the next generation of Amazon SageMaker by G2 Krishnamoorthy and Rahul Pathak on 13 MAR 2025 in Amazon SageMaker , Amazon SageMaker Unified Studio , Analytics , Artificial Intelligence , Thought Leadership Permalink Comments Share At AWS re:Invent 2024, we announced the next generation of Amazon SageMaker , the center for all your data, analytics, and AI. Amazon SageMaker brings together widely adopted AWS machine learning (ML) and analytics capabilities and addresses the challenges of harnessing organizational data for analytics and AI through unified access to tools and data with governance built in. It enables teams to securely find, prepare, and collaborate on data assets and build analytics and AI applications through a single experience, accelerating the path from data to value. At the core of the next generation of Amazon SageMaker is Amazon SageMaker Unified Studio , a single data and AI development environment where you can find and access your organizationâs data and act on it using the best tool for the job across virtually any use case. We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started. Amazon SageMaker Unified Studio SageMaker Unified Studio brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR , AWS Glue , Amazon Athena , Amazon Redshift , Amazon Bedrock , and Amazon SageMaker AI . From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications. Governance features including fine-grained access control are built into SageMaker Unified Studio using Amazon SageMaker Catalog to help you meet enterprise security requirements across your entire data estate. Unified access to your data is provided by Amazon SageMaker Lakehouse , a unified, open, and secure data lakehouse built on Apache Iceberg open standards. Whether your data is stored in Amazon Simple Storage Service (Amazon S3) data lakes, Redshift data warehouses, or third-party and federated data sources, you can access it from one place and use it with Iceberg-compatible engines and tools. In addition, SageMaker Lakehouse now integrates with Amazon S3 Tables , the first cloud object store with native Apache Iceberg support, so you can use SageMaker Lakehouse to create, query, and process S3 Tables efficiently using various analytics engines in SageMaker Unified Studio as well as Iceberg-compatible engines like Apache Spark and PyIceberg. Capabilities from Amazon Bedrock are now generally available in SageMaker Unified Studio, allowing you to rapidly prototype, customize, and share generative AI applications in a governed environment. Users have an intuitive interface to access high-performing foundation models (FMs) in Amazon Bedrock, including the Amazon Nova model series, and the ability to create Agents, Flows, Knowledge Bases, and Guardrails with a few clicks. Amazon Q Developer , the most capable generative AI assistant for software development, can be used within SageMaker Unified Studio to streamline tasks across the data and AI development lifecycle, including code authoring, SQL generation, data discovery, and troubleshooting. A new integrated way of working The general availability of SageMaker Unified Studio represents another meaningful step in our journey to offer our customers a streamlined way to work with their data, whether for analytics or AI. Many of our customers have told us that you are building data-driven applications to guide business decisions, improve agility, and drive innovation, but that these applications are complex to build because they require collaboration across teams and the integration of data and tools. Not only is it time consuming for users to learn multiple development experiences, but because data, code, and other development artifacts are stored separately, it is challenging for users to understand how they interact with each other and to use them cohesively. Configuring and governing access is also a cumbersome manual process. To overcome these hurdles, many organizations are building bespoke integrations between services, tools, and homegrown access management systems. However, what you need is the flexibility to adopt the best services for your use case while empowering your data teams with a unified development experience. âAt Carrier, the next generation of Amazon SageMaker is transforming our enterprise data strategy by streamlining how we build and scale data products. SageMaker Unified Studioâs approach to data discovery, processing, and model development has significantly accelerated our lakehouse implementation. Most impressively, its seamless integration with our existing data catalog and built-in governance controls enables us to democratize data access while maintaining security standards, helping our teams rapidly deliver advanced analytics and AI solutions across the enterprise.â â Justin McDowell, Director Data Platform & Data Engineering, Carrier Millions of organizations trust AWS and utilize our comprehensive set of purpose-built analytics, AI/ML, and generative AI capabilities to power data-driven applications without compromising on performance, scale, or cost. Our goal for the next generation of Amazon SageMaker, including SageMaker Unified Studio, is to make data and AI workers more productive by providing access to all your data and tools in a single development environment. Building from a single data and AI development environment Letâs explore a common business challenge: increasing revenue through better lead generation. Consider an organization implementing an intelligent digital assistant on their website to engage with customersâa process that traditionally requires multiple tools and data sources. With SageMaker Unified Studio, this entire process can now be carried out within a single data and AI development environment. First, the data team uses the generative AI playground within SageMaker Unified Studio to quickly evaluate and select the best model for their customer interactions. They then create a project to house the tools and resources necessary for their use case and use Amazon Bedrock within the project to build and deploy a sophisticated virtual assistant that quickly begins qualifying leads through their website. To identify the most promising opportunities, the team develops a segmentation strategy. The data engineer asks Amazon Q Developer to identify datasets that contain lead data and uses zero-ETL integrations to bring the data into SageMaker Lakehouse. The data analyst then discovers it and creates a comprehensive view of their market. They use the SQL query editor to build out marketing segments, which they then write back to SageMaker Lakehouse, where they are available to other team members. Finally, the data scientist accesses the same dataset, which they use to train and deploy an automated lead scoring model using tools available from SageMaker AI. During the model development phase, they use Amazon Q Developerâs inline code authoring and troubleshooting capabilities to efficiently write error free-code in their JupyterLab notebook. The final model provides sales teams with the highest-value opportunities, which they can visualize in a business intelligence dashboard and take action on immediately. Reducing time-to-value in a unified environment What is remarkable about this example is that entire process happens in one integrated environment. Without SageMaker Unified Studio, the team would have had to work with multiple data sources, tools, and services, spending time learning multiple development environments, creating resources shares, and manually configuring access controls. The data engineer and data analyst would have worked in various data warehouses, data lakes, and analytics tools, the data scientist would have worked in an ML studio and notebook environment, and the application builder in a generative AI tool. Now, theyâre able to build and collaborate with their data and tools available in one experience, dramatically reducing time-to-value. Thatâs why weâre so excited about the next generation of Amazon SageMaker and the general availability of SageMaker Unified Studio. We believe that by putting everything you need for analytics and AI in one place, you can solve complex end-to-end problems more efficiently and get to innovative outcomes faster than ever before. Getting started with SageMaker Unified Studio To learn more, check out the following resources: Amazon SageMaker Amazon SageMaker Unified Studio Amazon SageMaker Lakehouse About the authors G2 Krishnamoorthy is VP of Analytics, leading AWS data lake services, data integration, Amazon OpenSearch Service, and Amazon QuickSight. Prior to his current role, G2 built and ran the Analytics and ML Platform at Facebook/Meta, and built various parts of the SQL Server database, Azure Analytics, and Azure ML at Microsoft. Rahul Pathak is VP of Relational Database Engines, leading Amazon Aurora, Amazon Redshift, and Amazon QLDB. Prior to his current role, he was VP of Analytics at AWS, where he worked across the entire AWS database portfolio. He has co-founded two companies, one focused on digital media analytics and the other on IP-geolocation. 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1.6));line-height:1.714;font-weight:400;font-family:Amazon Ember Mono,Consolas,Andale Mono WT,Andale Mono,Lucida Console,Lucida Sans Typewriter,DejaVu Sans Mono,Bitstream Vera Sans Mono,Liberation Mono,Nimbus Mono L,Monaco,Courier New,Courier,monospace}@media (min-width: 481px) and (max-width: 768px){[data-eb-6a8f3296] .rgft_286fbc8d.rgft_c6f92487{font-size:calc(.75rem * var(--font-size-multiplier, 1.6));line-height:2;font-weight:400}}@media (max-width: 480px){[data-eb-6a8f3296] .rgft_286fbc8d.rgft_c6f92487{font-size:calc(.625rem * var(--font-size-multiplier, 1.6));line-height:2.4;font-weight:400}}[data-eb-6a8f3296] [data-rg-lang=ar] .rgft_286fbc8d.rgft_c6f92487{font-family:AmazonEmberArabic,Helvetica,Arial,sans-serif}[data-eb-6a8f3296] [data-rg-lang=ja] .rgft_286fbc8d.rgft_c6f92487{font-family:ShinGo,\30d2\30e9\30ae\30ce\89d2\30b4 Pro W3,Hiragino Kaku Gothic Pro,Osaka,\30e1\30a4\30ea\30aa,Meiryo,\ff2d\ff33 \ff30\30b4\30b7\30c3\30af,MS PGothic,sans-serif}[data-eb-6a8f3296] [data-rg-lang=ko] .rgft_286fbc8d.rgft_c6f92487{font-family:NotoSansKR,Malgun Gothic,sans-serif}[data-eb-6a8f3296] [data-rg-lang=th] .rgft_286fbc8d.rgft_c6f92487{font-family:NotoSansThai,Helvetica,Arial,sans-serif}[data-eb-6a8f3296] [data-rg-lang=zh] .rgft_286fbc8d.rgft_c6f92487{font-family:NotoSansTC,Helvetica,Arial,Microsoft Yahei,\5fae\8f6f\96c5\9ed1,STXihei,\534e\6587\7ec6\9ed1,sans-serif}[data-eb-6a8f3296] .rgft_d27b4751{color:var(--rg-color-text-utility, #161D26)}[data-eb-6a8f3296] .rgft_d27b4751.rgft_927d7fd1{font-size:calc(1rem * var(--font-size-multiplier, 1.6));line-height:1.5;font-weight:400;font-family:Amazon Ember Mono,Consolas,Andale Mono WT,Andale Mono,Lucida Console,Lucida Sans Typewriter,DejaVu Sans Mono,Bitstream Vera Sans Mono,Liberation Mono,Nimbus Mono L,Monaco,Courier New,Courier,monospace}@media (min-width: 481px) and (max-width: 768px){[data-eb-6a8f3296] .rgft_d27b4751.rgft_927d7fd1{font-size:calc(1rem * var(--font-size-multiplier, 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.rgft_d27b4751.rgft_100c8a76{font-size:calc(.875rem * var(--font-size-multiplier, 1.6));line-height:1.429;font-weight:400;font-family:Amazon Ember Mono,Consolas,Andale Mono WT,Andale Mono,Lucida Console,Lucida Sans Typewriter,DejaVu Sans Mono,Bitstream Vera Sans Mono,Liberation Mono,Nimbus Mono L,Monaco,Courier New,Courier,monospace}@media (min-width: 481px) and (max-width: 768px){[data-eb-6a8f3296] .rgft_d27b4751.rgft_100c8a76{font-size:calc(.875rem * var(--font-size-multiplier, 1.6));line-height:1.429;font-weight:400}}@media (max-width: 480px){[data-eb-6a8f3296] .rgft_d27b4751.rgft_100c8a76{font-size:calc(.875rem * var(--font-size-multiplier, 1.6));line-height:1.429;font-weight:400}}[data-eb-6a8f3296] [data-rg-lang=ar] .rgft_d27b4751.rgft_100c8a76{font-family:AmazonEmberArabic,Helvetica,Arial,sans-serif}[data-eb-6a8f3296] [data-rg-lang=ja] .rgft_d27b4751.rgft_100c8a76{font-family:ShinGo,\30d2\30e9\30ae\30ce\89d2\30b4 Pro W3,Hiragino Kaku Gothic Pro,Osaka,\30e1\30a4\30ea\30aa,Meiryo,\ff2d\ff33 \ff30\30b4\30b7\30c3\30af,MS PGothic,sans-serif}[data-eb-6a8f3296] [data-rg-lang=ko] .rgft_d27b4751.rgft_100c8a76{font-family:NotoSansKR,Malgun Gothic,sans-serif}[data-eb-6a8f3296] [data-rg-lang=th] .rgft_d27b4751.rgft_100c8a76{font-family:NotoSansT | 2026-01-13T09:29:12 |
https://cgit.freebsd.org/ports/blame/shells/fish/Makefile.crates | Makefile.crates « fish « shells - ports - FreeBSD ports tree index : ports 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 2024Q1 2024Q2 2024Q3 2024Q4 2025Q1 2025Q2 2025Q3 2025Q4 main FreeBSD ports tree about summary refs log blame commit diff log msg author committer range path: root / shells / fish / Makefile.crates blob: 6d771bdb6c66d52b6c1f05026f148bfc9901800d ( plain ) ( tree ) 2eb572527994 5ada834187a8 936afc0f085e 2eb572527994 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 936afc0f085e 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 936afc0f085e 2eb572527994 936afc0f085e 2eb572527994 5ada834187a8 2eb572527994 936afc0f085e 5ada834187a8 936afc0f085e 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 5ada834187a8 2eb572527994 92709b8ef6ce 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 CARGO_CRATES = aho-corasick-1.1 .4 \ allocator-api2-0.2 .21 \ autocfg-1.5 .0 \ bitflags-2.10 .0 \ block-buffer-0.10 .4 \ bstr-1.12 .1 \ cc-1.2 .51 \ cfg-if-1.0 .4 \ cfg_aliases-0.2 .1 \ cpufeatures-0.2 .17 \ crypto-common-0.1 .7 \ digest-0.10 .7 \ dirs-6.0 .0 \ dirs-sys-0.5 .0 \ equivalent-1.0 .2 \ errno-0.3 .14 \ fastrand-2.3 .0 \ find-msvc-tools-0.1 .6 \ fnv-1.0 .7 \ foldhash-0.2 .0 \ generic-array-0.14 .7 \ getrandom-0.2 .16 \ getrandom-0.3 .4 \ globset-0.4 .18 \ hashbrown-0.16 .1 \ jobserver-0.1 .34 \ libc-0.2 .178 \ libredox-0.1 .11 \ lock_api-0.4 .14 \ log-0.4 .29 \ lru-0.16 .2 \ macro_rules_attribute-0.2 .2 \ macro_rules_attribute-proc_macro-0.2 .2 \ memchr-2.7 .6 \ minimal-lexical-0.2 .1 \ nix-0.30 .1 \ nom-7.1 .3 \ num-traits-0.2 .19 \ once_cell-1.21 .3 \ option-ext-0.2 .0 \ parking_lot-0.12 .5 \ parking_lot_core-0.9 .12 \ paste-1.0 .15 \ phf-0.11 .3 \ phf-0.13 .1 \ phf_codegen-0.11 .3 \ phf_codegen-0.13 .1 \ phf_generator-0.11 .3 \ phf_generator-0.13 .1 \ phf_shared-0.11 .3 \ phf_shared-0.13 .1 \ pkg-config-0.3 .32 \ portable-atomic-1.12 .0 \ ppv-lite86-0.2 .21 \ proc-macro2-1.0 .103 \ quote-1.0 .42 \ r-efi-5.3 .0 \ rand-0.8 .5 \ rand-0.9 .2 \ rand_chacha-0.9 .0 \ rand_core-0.6 .4 \ rand_core-0.9 .3 \ redox_syscall-0.5 .18 \ redox_users-0.5 .2 \ regex-automata-0.4 .13 \ regex-syntax-0.8 .8 \ rsconf-0.3 .0 \ rust-embed-8.9 .0 \ rust-embed-impl-8.9 .0 \ rust-embed-utils-8.9 .0 \ same-file-1.0 .6 \ scc-2.4 .0 \ scopeguard-1.2 .0 \ sdd-3.0 .10 \ serde-1.0 .228 \ serde_core-1.0 .228 \ serde_derive-1.0 .228 \ serial_test-3.2 .0 \ serial_test_derive-3.2 .0 \ sha2-0.10 .9 \ shellexpand-3.1 .1 \ shlex-1.3 .0 \ siphasher-1.0 .1 \ smallvec-1.15 .1 \ syn-2.0 .111 \ terminfo-0.9 .0 \ thiserror-2.0 .17 \ thiserror-impl-2.0 .17 \ typenum-1.19 .0 \ unicode-ident-1.0 .22 \ unicode-segmentation-1.12 .0 \ unicode-width-0.2 .2 \ unix_path-1.0 .1 \ unix_str-1.0 .0 \ version_check-0.9 .5 \ walkdir-2.5 .0 \ wasi-0.11 .1 + wasi-snapshot-preview1 \ wasip2-1.0 .1 + wasi-0.2 .4 \ widestring-1.2 .1 \ winapi-util-0.1 .11 \ windows-link-0.2 .1 \ windows-sys-0.61 .2 \ wit-bindgen-0.46 .0 \ xterm-color-1.0 .1 \ zerocopy-0.8 .31 \ zerocopy-derive-0.8 .31 \ pcre2 , pcre2-sys@git + https :// github.com / fish-shell / rust-pcre2?tag = 0.2.9 - utf32\ #85b7afba1a9d9bd445779800e5bcafeb732e4421 generated by cgit v1.2.3 ( git 2.25.1 ) at 2026-01-13 09:29:12 +0000 | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/analytics/amazon-athena/page/2/ | Amazon Athena | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Athena Accelerate your analytics with Amazon S3 Tables and Amazon SageMaker Lakehouse by Sandeep Adwankar , Aditya Kalyanakrishnan , and Srividya Parthasarathy on 17 APR 2025 in Amazon Athena , Amazon Redshift , Amazon SageMaker Lakehouse , Amazon SageMaker Studio , Amazon Simple Storage Service (S3) , Analytics , Announcements , AWS Glue , AWS Identity and Access Management (IAM) , AWS Lake Formation Permalink Comments Share Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables. Enhancing Adobe Marketo Engage Data Analysis with AWS Glue Integration by Kenny Rajan , Kamen Sharlandjiev , Basheer Sheriff , and Rafal Pawlaszek on 11 MAR 2025 in Amazon Athena , Analytics , AWS Glue , Partner solutions Permalink Share In this post, we show you how to use AWS Glue to extract data from Marketo Engage for data processing and enrichment on AWS for use in marketing analytics workflows. Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity by Chiho Sugimoto , Shubham Agrawal , Joju Eruppanal , Noritaka Sekiyama , and Julie Zhao on 16 DEC 2024 in Amazon Athena , Amazon SageMaker , Analytics , AWS Glue Permalink Comments Share With Amazon SageMaker Lakehouse unified data connectivity, you can confidently connect, explore, and unlock the full value of your data across AWS services and achieve your business objectives with agility. This post demonstrates how SageMaker Lakehouse unified data connectivity helps your data integration workload by streamlining the establishment and management of connections for various data sources. Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt by Nancy Wu , Xu Feng , and Da Xu on 12 DEC 2024 in Amazon Athena , Amazon DataZone , Amazon Neptune , Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , AWS Lambda , AWS Step Functions , Technical How-to Permalink Comments Share In this post, we use dbt for data modeling on both Amazon Athena and Amazon Redshift. dbt on Athena supports real-time queries, while dbt on Amazon Redshift handles complex queries, unifying the development language and significantly reducing the technical learning curve. Using a single dbt modeling language not only simplifies the development process but also automatically generates consistent data lineage information. This approach offers robust adaptability, easily accommodating changes in data structures. Catalog and govern Amazon Athena federated queries with Amazon SageMaker Lakehouse by Sandeep Adwankar , Stuti Deshpande , Praveen Kumar , Scott Rigney , and Noritaka Sekiyama on 04 DEC 2024 in Amazon Athena , Amazon SageMaker , Analytics Permalink Comments Share In this post, we show how to connect to, govern, and run federated queries on data stored in Redshift, DynamoDB (Preview), and Snowflake (Preview). To query our data, we use Athena, which is seamlessly integrated with SageMaker Unified Studio. We use SageMaker Lakehouse to present data to end-users as federated catalogs, a new type of catalog object. Finally, we demonstrate how to use column-level security permissions in AWS Lake Formation to give analysts access to the data they need while restricting access to sensitive information. How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes by Leo Ramsamy , Rada Stanic , and Srinivasan Kuppusamy on 04 DEC 2024 in Amazon Athena , Amazon DataZone , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Quick Sight , Amazon Redshift , Amazon Simple Storage Service (S3) , Analytics , Architecture , AWS Glue , AWS Lake Formation , Best Practices , Customer Solutions , Thought Leadership Permalink Comments Share ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud by Darshit Thakkar , BP Yau , and Selman Ay on 22 NOV 2024 in Amazon Athena , Analytics , Announcements Permalink Comments Share We are excited to announce that the dbt adapter for Amazon Athena is now officially supported in dbt Cloud. This integration enables data teams to efficiently transform and manage data using Athena with dbt Cloudâs robust features, enhancing the overall data workflow experience. In this post, we discuss the advantages of dbt Cloud over dbt Core, common use cases, and how to get started with Amazon Athena using the dbt adapter. Streamline AI-driven analytics with governance: Integrating Tableau with Amazon DataZone by Ramesh H Singh , Adiascar Cisneros , Ariana Rahgozar , Yogesh Dhimate , and Joel Farvault on 30 OCT 2024 in Amazon Athena , Amazon DataZone , Analytics , Announcements Permalink Comments Share Amazon DataZone recently announced the expansion of data analysis and visualization options for your project-subscribed data within Amazon DataZone using the Amazon Athena JDBC driver. In this post, you learn how the recent enhancements in Amazon DataZone facilitate a seamless connection with Tableau. By integrating Tableau with the comprehensive data governance capabilities of Amazon DataZone, weâre empowering data consumers to quickly and seamlessly explore and analyze their governed data. Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more by Ramesh H Singh , Eric Fleishman , Fabricio Hamada , Joel Farvault , Lakshmi Nair , Lionel Pulickal , and Theo Tolv on 30 OCT 2024 in Amazon Athena , Amazon DataZone , Analytics , Intermediate (200) , Launch Permalink Comments Share Amazon DataZone now launched authentication support through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. This integration empowers data users to access and analyze governed data within Amazon DataZone using familiar tools, boosting both productivity and flexibility. Analyze Amazon EMR on Amazon EC2 cluster usage with Amazon Athena and Amazon QuickSight by Boon Lee Eu , Kyara Labrador , Vikas Omer , and Lorenzo Ripani on 25 OCT 2024 in Amazon Athena , Amazon EC2 , Amazon EMR , Amazon Quick Sight , Technical How-to Permalink Comments Share In this post, we guide you through deploying a comprehensive solution in your Amazon Web Services (AWS) environment to analyze Amazon EMR on EC2 cluster usage. By using this solution, you will gain a deep understanding of resource consumption and associated costs of individual applications running on your EMR cluster. â Older posts Newer posts â {"data":{"items":[{"fields":{"footer":"{\n \"createAccountButtonLabel\": \"Create an AWS account\",\n \"createAccountButtonURL\": \"https://portal.aws.amazon.com/gp/aws/developer/registration/index.html?nc1=f_ct&src=footer_signup\",\n \"backToTopText\": \"Back to top\",\n \"eoeText\": \"Amazon is an Equal Opportunity Employer: Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.\",\n \"copyrightText\": \"© 2025, Amazon Web Services, Inc. or its affiliates. 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https://cgit.freebsd.org/ports/blame/shells/fish/distinfo | distinfo « fish « shells - ports - FreeBSD ports tree index : ports 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 2024Q1 2024Q2 2024Q3 2024Q4 2025Q1 2025Q2 2025Q3 2025Q4 main FreeBSD ports tree about summary refs log blame commit diff log msg author committer range path: root / shells / fish / distinfo blob: f7bcc3d9bbc069b8adddbf3a75a9cb6f4e32e093 ( plain ) ( tree ) 2eb572527994 5ada834187a8 936afc0f085e 2eb572527994 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 5ada834187a8 2eb572527994 936afc0f085e 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 2eb572527994 5ada834187a8 936afc0f085e 5ada834187a8 936afc0f085e 5ada834187a8 2eb572527994 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177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 TIMESTAMP = 1767794267 SHA256 (fish-4.3.3.tar.xz) = eba0e7d325c1d46046bb9d29434e7e0dabf7f584eb156845005d688e10b86e57 SIZE (fish-4.3.3.tar.xz) = 2545388 SHA256 (rust/crates/aho-corasick-1.1.4.crate) = ddd31a130427c27518df266943a5308ed92d4b226cc639f5a8f1002816174301 SIZE (rust/crates/aho-corasick-1.1.4.crate) = 184015 SHA256 (rust/crates/allocator-api2-0.2.21.crate) = 683d7910e743518b0e34f1186f92494becacb047c7b6bf616c96772180fef923 SIZE (rust/crates/allocator-api2-0.2.21.crate) = 63622 SHA256 (rust/crates/autocfg-1.5.0.crate) = c08606f8c3cbf4ce6ec8e28fb0014a2c086708fe954eaa885384a6165172e7e8 SIZE (rust/crates/autocfg-1.5.0.crate) = 18729 SHA256 (rust/crates/bitflags-2.10.0.crate) = 812e12b5285cc515a9c72a5c1d3b6d46a19dac5acfef5265968c166106e31dd3 SIZE (rust/crates/bitflags-2.10.0.crate) = 48427 SHA256 (rust/crates/block-buffer-0.10.4.crate) = 3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71 SIZE (rust/crates/block-buffer-0.10.4.crate) = 10538 SHA256 (rust/crates/bstr-1.12.1.crate) = 63044e1ae8e69f3b5a92c736ca6269b8d12fa7efe39bf34ddb06d102cf0e2cab SIZE (rust/crates/bstr-1.12.1.crate) = 354916 SHA256 (rust/crates/cc-1.2.51.crate) = 7a0aeaff4ff1a90589618835a598e545176939b97874f7abc7851caa0618f203 SIZE (rust/crates/cc-1.2.51.crate) = 93352 SHA256 (rust/crates/cfg-if-1.0.4.crate) = 9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801 SIZE (rust/crates/cfg-if-1.0.4.crate) = 9360 SHA256 (rust/crates/cfg_aliases-0.2.1.crate) = 613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724 SIZE (rust/crates/cfg_aliases-0.2.1.crate) = 6355 SHA256 (rust/crates/cpufeatures-0.2.17.crate) = 59ed5838eebb26a2bb2e58f6d5b5316989ae9d08bab10e0e6d103e656d1b0280 SIZE (rust/crates/cpufeatures-0.2.17.crate) = 13466 SHA256 (rust/crates/crypto-common-0.1.7.crate) = 78c8292055d1c1df0cce5d180393dc8cce0abec0a7102adb6c7b1eef6016d60a SIZE (rust/crates/crypto-common-0.1.7.crate) = 9619 SHA256 (rust/crates/digest-0.10.7.crate) = 9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292 SIZE (rust/crates/digest-0.10.7.crate) = 19557 SHA256 (rust/crates/dirs-6.0.0.crate) = c3e8aa94d75141228480295a7d0e7feb620b1a5ad9f12bc40be62411e38cce4e SIZE (rust/crates/dirs-6.0.0.crate) = 14190 SHA256 (rust/crates/dirs-sys-0.5.0.crate) = e01a3366d27ee9890022452ee61b2b63a67e6f13f58900b651ff5665f0bb1fab SIZE (rust/crates/dirs-sys-0.5.0.crate) = 10157 SHA256 (rust/crates/equivalent-1.0.2.crate) = 877a4ace8713b0bcf2a4e7eec82529c029f1d0619886d18145fea96c3ffe5c0f SIZE (rust/crates/equivalent-1.0.2.crate) = 7419 SHA256 (rust/crates/errno-0.3.14.crate) = 39cab71617ae0d63f51a36d69f866391735b51691dbda63cf6f96d042b63efeb SIZE (rust/crates/errno-0.3.14.crate) = 12002 SHA256 (rust/crates/fastrand-2.3.0.crate) = 37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be SIZE (rust/crates/fastrand-2.3.0.crate) = 15076 SHA256 (rust/crates/find-msvc-tools-0.1.6.crate) = 645cbb3a84e60b7531617d5ae4e57f7e27308f6445f5abf653209ea76dec8dff SIZE (rust/crates/find-msvc-tools-0.1.6.crate) = 30978 SHA256 (rust/crates/fnv-1.0.7.crate) = 3f9eec918d3f24069decb9af1554cad7c880e2da24a9afd88aca000531ab82c1 SIZE (rust/crates/fnv-1.0.7.crate) = 11266 SHA256 (rust/crates/foldhash-0.2.0.crate) = 77ce24cb58228fbb8aa041425bb1050850ac19177686ea6e0f41a70416f56fdb SIZE (rust/crates/foldhash-0.2.0.crate) = 23329 SHA256 (rust/crates/generic-array-0.14.7.crate) = 85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a SIZE (rust/crates/generic-array-0.14.7.crate) = 15950 SHA256 (rust/crates/getrandom-0.2.16.crate) = 335ff9f135e4384c8150d6f27c6daed433577f86b4750418338c01a1a2528592 SIZE (rust/crates/getrandom-0.2.16.crate) = 40163 SHA256 (rust/crates/getrandom-0.3.4.crate) = 899def5c37c4fd7b2664648c28120ecec138e4d395b459e5ca34f9cce2dd77fd SIZE (rust/crates/getrandom-0.3.4.crate) = 50932 SHA256 (rust/crates/globset-0.4.18.crate) = 52dfc19153a48bde0cbd630453615c8151bce3a5adfac7a0aebfbf0a1e1f57e3 SIZE (rust/crates/globset-0.4.18.crate) = 28970 SHA256 (rust/crates/hashbrown-0.16.1.crate) = 841d1cc9bed7f9236f321df977030373f4a4163ae1a7dbfe1a51a2c1a51d9100 SIZE (rust/crates/hashbrown-0.16.1.crate) = 147785 SHA256 (rust/crates/jobserver-0.1.34.crate) = 9afb3de4395d6b3e67a780b6de64b51c978ecf11cb9a462c66be7d4ca9039d33 SIZE (rust/crates/jobserver-0.1.34.crate) = 29013 SHA256 (rust/crates/libc-0.2.178.crate) = 37c93d8daa9d8a012fd8ab92f088405fb202ea0b6ab73ee2482ae66af4f42091 SIZE (rust/crates/libc-0.2.178.crate) = 783720 SHA256 (rust/crates/libredox-0.1.11.crate) = df15f6eac291ed1cf25865b1ee60399f57e7c227e7f51bdbd4c5270396a9ed50 SIZE (rust/crates/libredox-0.1.11.crate) = 7330 SHA256 (rust/crates/lock_api-0.4.14.crate) = 224399e74b87b5f3557511d98dff8b14089b3dadafcab6bb93eab67d3aace965 SIZE (rust/crates/lock_api-0.4.14.crate) = 29249 SHA256 (rust/crates/log-0.4.29.crate) = 5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897 SIZE (rust/crates/log-0.4.29.crate) = 51515 SHA256 (rust/crates/lru-0.16.2.crate) = 96051b46fc183dc9cd4a223960ef37b9af631b55191852a8274bfef064cda20f SIZE (rust/crates/lru-0.16.2.crate) = 16627 SHA256 (rust/crates/macro_rules_attribute-0.2.2.crate) = 65049d7923698040cd0b1ddcced9b0eb14dd22c5f86ae59c3740eab64a676520 SIZE (rust/crates/macro_rules_attribute-0.2.2.crate) = 15330 SHA256 (rust/crates/macro_rules_attribute-proc_macro-0.2.2.crate) = 670fdfda89751bc4a84ac13eaa63e205cf0fd22b4c9a5fbfa085b63c1f1d3a30 SIZE (rust/crates/macro_rules_attribute-proc_macro-0.2.2.crate) = 8271 SHA256 (rust/crates/memchr-2.7.6.crate) = f52b00d39961fc5b2736ea853c9cc86238e165017a493d1d5c8eac6bdc4cc273 SIZE (rust/crates/memchr-2.7.6.crate) = 97616 SHA256 (rust/crates/minimal-lexical-0.2.1.crate) = 68354c5c6bd36d73ff3feceb05efa59b6acb7626617f4962be322a825e61f79a SIZE (rust/crates/minimal-lexical-0.2.1.crate) = 94841 SHA256 (rust/crates/nix-0.30.1.crate) = 74523f3a35e05aba87a1d978330aef40f67b0304ac79c1c00b294c9830543db6 SIZE (rust/crates/nix-0.30.1.crate) = 342015 SHA256 (rust/crates/nom-7.1.3.crate) = d273983c5a657a70a3e8f2a01329822f3b8c8172b73826411a55751e404a0a4a SIZE (rust/crates/nom-7.1.3.crate) = 117570 SHA256 (rust/crates/num-traits-0.2.19.crate) = 071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841 SIZE (rust/crates/num-traits-0.2.19.crate) = 51631 SHA256 (rust/crates/once_cell-1.21.3.crate) = 42f5e15c9953c5e4ccceeb2e7382a716482c34515315f7b03532b8b4e8393d2d SIZE (rust/crates/once_cell-1.21.3.crate) = 34534 SHA256 (rust/crates/option-ext-0.2.0.crate) = 04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d SIZE (rust/crates/option-ext-0.2.0.crate) = 7345 SHA256 (rust/crates/parking_lot-0.12.5.crate) = 93857453250e3077bd71ff98b6a65ea6621a19bb0f559a85248955ac12c45a1a SIZE (rust/crates/parking_lot-0.12.5.crate) = 46735 SHA256 (rust/crates/parking_lot_core-0.9.12.crate) = 2621685985a2ebf1c516881c026032ac7deafcda1a2c9b7850dc81e3dfcb64c1 SIZE (rust/crates/parking_lot_core-0.9.12.crate) = 34110 SHA256 (rust/crates/paste-1.0.15.crate) = 57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a SIZE (rust/crates/paste-1.0.15.crate) = 18374 SHA256 (rust/crates/phf-0.11.3.crate) = 1fd6780a80ae0c52cc120a26a1a42c1ae51b247a253e4e06113d23d2c2edd078 SIZE (rust/crates/phf-0.11.3.crate) = 23231 SHA256 (rust/crates/phf-0.13.1.crate) = c1562dc717473dbaa4c1f85a36410e03c047b2e7df7f45ee938fbef64ae7fadf SIZE (rust/crates/phf-0.13.1.crate) = 24786 SHA256 (rust/crates/phf_codegen-0.11.3.crate) = aef8048c789fa5e851558d709946d6d79a8ff88c0440c587967f8e94bfb1216a SIZE (rust/crates/phf_codegen-0.11.3.crate) = 13741 SHA256 (rust/crates/phf_codegen-0.13.1.crate) = 49aa7f9d80421bca176ca8dbfebe668cc7a2684708594ec9f3c0db0805d5d6e1 SIZE (rust/crates/phf_codegen-0.13.1.crate) = 14774 SHA256 (rust/crates/phf_generator-0.11.3.crate) = 3c80231409c20246a13fddb31776fb942c38553c51e871f8cbd687a4cfb5843d SIZE (rust/crates/phf_generator-0.11.3.crate) = 15431 SHA256 (rust/crates/phf_generator-0.13.1.crate) = 135ace3a761e564ec88c03a77317a7c6b80bb7f7135ef2544dbe054243b89737 SIZE (rust/crates/phf_generator-0.13.1.crate) = 15952 SHA256 (rust/crates/phf_shared-0.11.3.crate) = 67eabc2ef2a60eb7faa00097bd1ffdb5bd28e62bf39990626a582201b7a754e5 SIZE (rust/crates/phf_shared-0.11.3.crate) = 15199 SHA256 (rust/crates/phf_shared-0.13.1.crate) = e57fef6bc5981e38c2ce2d63bfa546861309f875b8a75f092d1d54ae2d64f266 SIZE (rust/crates/phf_shared-0.13.1.crate) = 16141 SHA256 (rust/crates/pkg-config-0.3.32.crate) = 7edddbd0b52d732b21ad9a5fab5c704c14cd949e5e9a1ec5929a24fded1b904c SIZE (rust/crates/pkg-config-0.3.32.crate) = 21370 SHA256 (rust/crates/portable-atomic-1.12.0.crate) = f59e70c4aef1e55797c2e8fd94a4f2a973fc972cfde0e0b05f683667b0cd39dd SIZE (rust/crates/portable-atomic-1.12.0.crate) = 191124 SHA256 (rust/crates/ppv-lite86-0.2.21.crate) = 85eae3c4ed2f50dcfe72643da4befc30deadb458a9b590d720cde2f2b1e97da9 SIZE (rust/crates/ppv-lite86-0.2.21.crate) = 22522 SHA256 (rust/crates/proc-macro2-1.0.103.crate) = 5ee95bc4ef87b8d5ba32e8b7714ccc834865276eab0aed5c9958d00ec45f49e8 SIZE (rust/crates/proc-macro2-1.0.103.crate) = 60024 SHA256 (rust/crates/quote-1.0.42.crate) = a338cc41d27e6cc6dce6cefc13a0729dfbb81c262b1f519331575dd80ef3067f SIZE (rust/crates/quote-1.0.42.crate) = 31504 SHA256 (rust/crates/r-efi-5.3.0.crate) = 69cdb34c158ceb288df11e18b4bd39de994f6657d83847bdffdbd7f346754b0f SIZE (rust/crates/r-efi-5.3.0.crate) = 64532 SHA256 (rust/crates/rand-0.8.5.crate) = 34af8d1a0e25924bc5b7c43c079c942339d8f0a8b57c39049bef581b46327404 SIZE (rust/crates/rand-0.8.5.crate) = 87113 SHA256 (rust/crates/rand-0.9.2.crate) = 6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1 SIZE (rust/crates/rand-0.9.2.crate) = 99930 SHA256 (rust/crates/rand_chacha-0.9.0.crate) = d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb SIZE (rust/crates/rand_chacha-0.9.0.crate) = 18258 SHA256 (rust/crates/rand_core-0.6.4.crate) = ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c SIZE (rust/crates/rand_core-0.6.4.crate) = 22666 SHA256 (rust/crates/rand_core-0.9.3.crate) = 99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38 SIZE (rust/crates/rand_core-0.9.3.crate) = 24543 SHA256 (rust/crates/redox_syscall-0.5.18.crate) = ed2bf2547551a7053d6fdfafda3f938979645c44812fbfcda098faae3f1a362d SIZE (rust/crates/redox_syscall-0.5.18.crate) = 30747 SHA256 (rust/crates/redox_users-0.5.2.crate) = a4e608c6638b9c18977b00b475ac1f28d14e84b27d8d42f70e0bf1e3dec127ac SIZE (rust/crates/redox_users-0.5.2.crate) = 17280 SHA256 (rust/crates/regex-automata-0.4.13.crate) = 5276caf25ac86c8d810222b3dbb938e512c55c6831a10f3e6ed1c93b84041f1c SIZE (rust/crates/regex-automata-0.4.13.crate) = 625250 SHA256 (rust/crates/regex-syntax-0.8.8.crate) = 7a2d987857b319362043e95f5353c0535c1f58eec5336fdfcf626430af7def58 SIZE (rust/crates/regex-syntax-0.8.8.crate) = 359141 SHA256 (rust/crates/rsconf-0.3.0.crate) = 06cbd984e96cc891aa018958ac3d09986c0ea7635eedfff670b99a90970f159f SIZE (rust/crates/rsconf-0.3.0.crate) = 25832 SHA256 (rust/crates/rust-embed-8.9.0.crate) = 947d7f3fad52b283d261c4c99a084937e2fe492248cb9a68a8435a861b8798ca SIZE (rust/crates/rust-embed-8.9.0.crate) = 900777 SHA256 (rust/crates/rust-embed-impl-8.9.0.crate) = 5fa2c8c9e8711e10f9c4fd2d64317ef13feaab820a4c51541f1a8c8e2e851ab2 SIZE (rust/crates/rust-embed-impl-8.9.0.crate) = 9279 SHA256 (rust/crates/rust-embed-utils-8.9.0.crate) = 60b161f275cb337fe0a44d924a5f4df0ed69c2c39519858f931ce61c779d3475 SIZE (rust/crates/rust-embed-utils-8.9.0.crate) = 5730 SHA256 (rust/crates/same-file-1.0.6.crate) = 93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502 SIZE (rust/crates/same-file-1.0.6.crate) = 10183 SHA256 (rust/crates/scc-2.4.0.crate) = 46e6f046b7fef48e2660c57ed794263155d713de679057f2d0c169bfc6e756cc SIZE (rust/crates/scc-2.4.0.crate) = 145014 SHA256 (rust/crates/scopeguard-1.2.0.crate) = 94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49 SIZE (rust/crates/scopeguard-1.2.0.crate) = 11619 SHA256 (rust/crates/sdd-3.0.10.crate) = 490dcfcbfef26be6800d11870ff2df8774fa6e86d047e3e8c8a76b25655e41ca SIZE (rust/crates/sdd-3.0.10.crate) = 32748 SHA256 (rust/crates/serde-1.0.228.crate) = 9a8e94ea7f378bd32cbbd37198a4a91436180c5bb472411e48b5ec2e2124ae9e SIZE (rust/crates/serde-1.0.228.crate) = 83652 SHA256 (rust/crates/serde_core-1.0.228.crate) = 41d385c7d4ca58e59fc732af25c3983b67ac852c1a25000afe1175de458b67ad SIZE (rust/crates/serde_core-1.0.228.crate) = 63111 SHA256 (rust/crates/serde_derive-1.0.228.crate) = d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79 SIZE (rust/crates/serde_derive-1.0.228.crate) = 59605 SHA256 (rust/crates/serial_test-3.2.0.crate) = 1b258109f244e1d6891bf1053a55d63a5cd4f8f4c30cf9a1280989f80e7a1fa9 SIZE (rust/crates/serial_test-3.2.0.crate) = 9232 SHA256 (rust/crates/serial_test_derive-3.2.0.crate) = 5d69265a08751de7844521fd15003ae0a888e035773ba05695c5c759a6f89eef SIZE (rust/crates/serial_test_derive-3.2.0.crate) = 6992 SHA256 (rust/crates/sha2-0.10.9.crate) = a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283 SIZE (rust/crates/sha2-0.10.9.crate) = 29271 SHA256 (rust/crates/shellexpand-3.1.1.crate) = 8b1fdf65dd6331831494dd616b30351c38e96e45921a27745cf98490458b90bb SIZE (rust/crates/shellexpand-3.1.1.crate) = 25904 SHA256 (rust/crates/shlex-1.3.0.crate) = 0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64 SIZE (rust/crates/shlex-1.3.0.crate) = 18713 SHA256 (rust/crates/siphasher-1.0.1.crate) = 56199f7ddabf13fe5074ce809e7d3f42b42ae711800501b5b16ea82ad029c39d SIZE (rust/crates/siphasher-1.0.1.crate) = 10351 SHA256 (rust/crates/smallvec-1.15.1.crate) = 67b1b7a3b5fe4f1376887184045fcf45c69e92af734b7aaddc05fb777b6fbd03 SIZE (rust/crates/smallvec-1.15.1.crate) = 38116 SHA256 (rust/crates/syn-2.0.111.crate) = 390cc9a294ab71bdb1aa2e99d13be9c753cd2d7bd6560c77118597410c4d2e87 SIZE (rust/crates/syn-2.0.111.crate) = 302117 SHA256 (rust/crates/terminfo-0.9.0.crate) = d4ea810f0692f9f51b382fff5893887bb4580f5fa246fde546e0b13e7fcee662 SIZE (rust/crates/terminfo-0.9.0.crate) = 45420 SHA256 (rust/crates/thiserror-2.0.17.crate) = f63587ca0f12b72a0600bcba1d40081f830876000bb46dd2337a3051618f4fc8 SIZE (rust/crates/thiserror-2.0.17.crate) = 28857 SHA256 (rust/crates/thiserror-impl-2.0.17.crate) = 3ff15c8ecd7de3849db632e14d18d2571fa09dfc5ed93479bc4485c7a517c913 SIZE (rust/crates/thiserror-impl-2.0.17.crate) = 21344 SHA256 (rust/crates/typenum-1.19.0.crate) = 562d481066bde0658276a35467c4af00bdc6ee726305698a55b86e61d7ad82bb SIZE (rust/crates/typenum-1.19.0.crate) = 76414 SHA256 (rust/crates/unicode-ident-1.0.22.crate) = 9312f7c4f6ff9069b165498234ce8be658059c6728633667c526e27dc2cf1df5 SIZE (rust/crates/unicode-ident-1.0.22.crate) = 47919 SHA256 (rust/crates/unicode-segmentation-1.12.0.crate) = f6ccf251212114b54433ec949fd6a7841275f9ada20dddd2f29e9ceea4501493 SIZE (rust/crates/unicode-segmentation-1.12.0.crate) = 106323 SHA256 (rust/crates/unicode-width-0.2.2.crate) = b4ac048d71ede7ee76d585517add45da530660ef4390e49b098733c6e897f254 SIZE (rust/crates/unicode-width-0.2.2.crate) = 282768 SHA256 (rust/crates/unix_path-1.0.1.crate) = af8e291873ae77c4c8d9c9b34d0bee68a35b048fb39c263a5155e0e353783eaf SIZE (rust/crates/unix_path-1.0.1.crate) = 22226 SHA256 (rust/crates/unix_str-1.0.0.crate) = 2ace0b4755d0a2959962769239d56267f8a024fef2d9b32666b3dcd0946b0906 SIZE (rust/crates/unix_str-1.0.0.crate) = 16226 SHA256 (rust/crates/version_check-0.9.5.crate) = 0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a SIZE (rust/crates/version_check-0.9.5.crate) = 15554 SHA256 (rust/crates/walkdir-2.5.0.crate) = 29790946404f91d9c5d06f9874efddea1dc06c5efe94541a7d6863108e3a5e4b SIZE (rust/crates/walkdir-2.5.0.crate) = 23951 SHA256 (rust/crates/wasi-0.11.1+wasi-snapshot-preview1.crate) = ccf3ec651a847eb01de73ccad15eb7d99f80485de043efb2f370cd654f4ea44b SIZE (rust/crates/wasi-0.11.1+wasi-snapshot-preview1.crate) = 28477 SHA256 (rust/crates/wasip2-1.0.1+wasi-0.2.4.crate) = 0562428422c63773dad2c345a1882263bbf4d65cf3f42e90921f787ef5ad58e7 SIZE (rust/crates/wasip2-1.0.1+wasi-0.2.4.crate) = 132087 SHA256 (rust/crates/widestring-1.2.1.crate) = 72069c3113ab32ab29e5584db3c6ec55d416895e60715417b5b883a357c3e471 SIZE (rust/crates/widestring-1.2.1.crate) = 90350 SHA256 (rust/crates/winapi-util-0.1.11.crate) = c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22 SIZE (rust/crates/winapi-util-0.1.11.crate) = 13368 SHA256 (rust/crates/windows-link-0.2.1.crate) = f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5 SIZE (rust/crates/windows-link-0.2.1.crate) = 6133 SHA256 (rust/crates/windows-sys-0.61.2.crate) = ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc SIZE (rust/crates/windows-sys-0.61.2.crate) = 2517186 SHA256 (rust/crates/wit-bindgen-0.46.0.crate) = f17a85883d4e6d00e8a97c586de764dabcc06133f7f1d55dce5cdc070ad7fe59 SIZE (rust/crates/wit-bindgen-0.46.0.crate) = 60508 SHA256 (rust/crates/xterm-color-1.0.1.crate) = 4de5f056fb9dc8b7908754867544e26145767187aaac5a98495e88ad7cb8a80f SIZE (rust/crates/xterm-color-1.0.1.crate) = 9185 SHA256 (rust/crates/zerocopy-0.8.31.crate) = fd74ec98b9250adb3ca554bdde269adf631549f51d8a8f8f0a10b50f1cb298c3 SIZE (rust/crates/zerocopy-0.8.31.crate) = 257633 SHA256 (rust/crates/zerocopy-derive-0.8.31.crate) = d8a8d209fdf45cf5138cbb5a506f6b52522a25afccc534d1475dad8e31105c6a SIZE (rust/crates/zerocopy-derive-0.8.31.crate) = 90835 SHA256 (fish-shell-rust-pcre2-0.2.9-utf32_GH0.tar.gz) = e5af06d7b737b66f7476a223e8a6cd1e2b1ca834b38b3de58901d4dbcf0a054d SIZE (fish-shell-rust-pcre2-0.2.9-utf32_GH0.tar.gz) = 742431 generated by cgit v1.2.3 ( git 2.25.1 ) at 2026-01-13 09:29:12 +0000 | 2026-01-13T09:29:12 |
https://cgit.freebsd.org/ports/refs/ | ports - FreeBSD ports tree index : ports 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 2024Q1 2024Q2 2024Q3 2024Q4 2025Q1 2025Q2 2025Q3 2025Q4 main FreeBSD ports tree about summary refs log tree commit diff log msg author committer range Branch Commit message Author Age main audio/exaile: update to 4.2.1 Ruslan Makhmatkhanov 12 min. 2025Q4 www/waterfox: Add ffmpeg8 support Martin Filla 12 hours 2025Q3 ports-mgmt/poudriere-devel: Update to 3.3.0-2209-g282bb7247 Bryan Drewery 3 months 2025Q2 security/sudo: security update to 1.9.17p1 FiLiS 6 months 2025Q1 math/hs-Agda: Disable building emacs mode files until upstream fixes it Gleb Popov 9 months 2024Q1 multimedia/obs-studio: Broken on armv7 Yuri Victorovich 11 months 2024Q4 games/mizuma: describe usability with KDE Plasma Alexander Vereeken 12 months 2024Q3 emulators/flexemu: Update to 3.22 Rainer Hurling 15 months 2024Q2 www/rt50: Update to 5.0.7 Mikael Urankar 18 months 2023Q4 emulators/yuzu: update to s20240102 Jan Beich 2 years 2023Q3 sysutils/synergy: Remove unnecessary double quotation Koichiro Iwao 2 years 2023Q2 www/mediawiki139: Update to 1.39.4 Wen Heping 3 years 2023Q1 graphics/mesa-devel: update to 23.0.b.4037 Jan Beich 3 years 2022Q4 graphics/mesa-devel: update to 22.3.b.2363 Jan Beich 3 years 2022Q3 www/chromium: update to 106.0.5249.91 Robert Nagy 3 years 2022Q2 graphics/mesa-devel: update to 22.1.b.3763 Jan Beich 4 years 2022Q1 misc/freebsd-release-manifests: prune 13.1-BETA3 Glen Barber 4 years 2021Q4 databases/sqlite3: unbreak fetch Charlie Li 4 years 2021Q3 emulators/yuzu: update to s20211002 Jan Beich 4 years 2021Q2 sysutils/py-salt: Partically revert f64d882074b90 Li-Wen Hsu 5 years 2021Q1 audio/faac: Update to 1.30; switch upstream fetch location to GitHub. Thomas Zander 5 years 2020Q4 MFH: r559986 Jan Beich 5 years 2020Q3 MFH: r550753 Christoph Moench-Tegeder 5 years 2020Q2 MFH: r535269 Jan Beich 6 years 2020Q1 MFH: r529993 Bryan Drewery 6 years 2019Q4 MFH: r521639 Glen Barber 6 years 2019Q3 MFH: r512436 Dimitry Andric 6 years 2019Q2 MFH: r505800 Jan Beich 7 years 2019Q1 MFH: r497166 Sunpoet Po-Chuan Hsieh 7 years 2018Q4 MFH: r488851 Jan Beich 7 years 2018Q3 devel/google{test,mock}: back out r480988, r480990, r480991 Jan Beich 7 years 2018Q2 MFH: r473670 Christoph Moench-Tegeder 8 years 2018Q1 MFH: r472125 Jan Beich 8 years 2017Q4 MFH: r451469 r457648 Danilo G. Baio 8 years 2017Q3 MFH: r450936 Jason E. Hale 8 years 2017Q2 MFH: r446250 Jan Beich 8 years 2017Q1 MFH: r436993 r437023 Sunpoet Po-Chuan Hsieh 9 years 2016Q4 MFH: r430138 Jason Unovitch 9 years 2016Q3 security/openssl-devel: Fix build in 2016Q3 branch Bernard Spil 9 years 2016Q2 MFH: r417847 Mark Felder 10 years 2016Q1 MFH: r405052 r407795 r408138 r410451 r412140 Olivier Duchateau 10 years 2015Q4 MFH: r404817 Raphael Kubo da Costa 10 years 2014Q4 MFH: r401586 Thomas Zander 10 years 2015Q3 MFH: r398371 Gordon Tetlow 10 years 2015Q2 Shibboleth SP software crashes on well-formed but invalid XML. Palle Girgensohn 10 years 2015Q1 MFH: r382722 Alonso Schaich 11 years 2014Q3 MFH: r369684 Bryan Drewery 11 years 2014Q2 MFH: r359687 Thomas Zander 12 years 2014Q1 MFH: r349499 Lars Engels 12 years Tag Download Author Age release/15.0.0 commit 715bf41195... Colin Percival 8 weeks 14.2-eol commit 4e450f33b9... Rene Ladan 3 months 13.4-eol commit ba96410bb8... Rene Ladan 6 months release/14.3.0 commit a764c06788... Colin Percival 8 months 14.1-eol commit 71ffb8bcea... Rene Ladan 9 months release/13.5.0 commit 2cbed77221... Colin Percival 10 months 13.3-eol commit bd3f32dcf8... Rene Ladan 12 months release/14.2.0 commit 019c097f52... Colin Percival 14 months 14.0-eol commit d5d82c5ff9... Rene Ladan 15 months release/13.4.0 commit 8103237c5a... Colin Percival 17 months release/14.1.0 commit 347f69f1d5... Colin Percival 20 months release/13.3.0 commit 941d96c740... Colin Percival 23 months 12-eol commit cd2fee9694... Muhammad Moinur Rahman 2 years 12.4-eol commit cd2fee9694... Muhammad Moinur Rahman 2 years release/14.0.0 commit 3854b733b2... Glen Barber 2 years 13.1-eol-q commit aaaacb3948... Rene Ladan 2 years 13.1-eol commit 6983bfc505... Rene Ladan 2 years 12.3-last commit 450bb37cf3... Rene Ladan 3 years 12.3-eol commit 56932296b7... Rene Ladan 3 years release/13.2.0 commit b2ae9aec98... Glen Barber 3 years release/12.4.0 commit 8ef5d08e2f... Glen Barber 3 years release/13.1.0 commit 5889de9086... Glen Barber 4 years release/12.3.0 commit 817404d72a... Glen Barber 4 years 11-eol commit 6a5a9cc088... Rene Ladan 4 years release/13.0.0 commit 8cbaa0affa... Glen Barber 5 years release/12.2.0 commit 78bf4d122a... Glen Barber 5 years release/11.4.0 commit 062557313a... Glen Barber 6 years release/12.1.0 commit 1e2c1a3948... Glen Barber 6 years release/11.3.0 commit 3e5caf5a86... Glen Barber 7 years release/12.0.0 commit 5ef93e5a36... Glen Barber 7 years 10-eol commit 8aa70a974c... Rene Ladan 7 years release/11.2.0 commit 83dbb3a052... Glen Barber 8 years release/10.4.0 commit 0b48d9e7b4... Glen Barber 8 years release/11.1.0 commit 138a1c801e... Glen Barber 9 years 9-eol commit 815bd83dba... Antoine Brodin 9 years release/11.0.0 commit db65520033... Bryan Drewery 9 years release/10.3.0 commit ce01c0b5c4... Bryan Drewery 10 years release/10.2.0 commit ee2a23efd4... Bryan Drewery 10 years 8-eol commit e1b8fc777f... Baptiste Daroussin 10 years release/10.1.0 commit 0a3dfe952b... Baptiste Daroussin 11 years pkg-install-eol commit 578815a950... Baptiste Daroussin 11 years release/9.3.0 commit 07dc148d71... Baptiste Daroussin 12 years release/10.0.0 commit a458e0d7db... Baptiste Daroussin 12 years release/9.2.0 commit 7aa7b253ba... Bryan Drewery 12 years release/8.4.0 commit 12cbabffa6... Martin Wilke 13 years 7-eol commit da12e6723d... Thomas Abthorpe 13 years release/9.1.0 commit d1f8cf169f... Beat Gaetzi 13 years release/8.3.0 commit 1d5dc71eb7... cvs2svn 14 years release/9.0.0 commit 91677839a2... cvs2svn 14 years release/8.2.0 commit b36e665e56... cvs2svn 15 years release/7.4.0 commit f259566108... cvs2svn 15 years 6-eol commit 058f99bb6b... cvs2svn 15 years release/8.1.0 commit b80064cbb9... cvs2svn 16 years release/7.3.0 commit 08acf5c407... cvs2svn 16 years release/8.0.0 commit cc415305a0... cvs2svn 16 years release/7.2.0 commit b6bb8c3d3a... cvs2svn 17 years release/7.1.0 commit 616aaf4b1f... cvs2svn 17 years release/6.4.0 commit f982be45e8... cvs2svn 17 years 5-eol commit a54fe1ca9c... cvs2svn 18 years release/7.0.0 commit fa4a943c17... cvs2svn 18 years release/6.3.0 commit 18a56320c9... cvs2svn 18 years pre-xorg-7 commit 06a27a0b36... cvs2svn 19 years 4-eol commit cec49cf8f4... cvs2svn 19 years release/6.2.0 commit f837432067... cvs2svn 19 years release/6.1.0 commit b47be74084... cvs2svn 20 years release/5.5.0 commit 96fe98e1c6... cvs2svn 20 years release/6.0.0 commit 40a3261d1a... cvs2svn 20 years release/5.4.0 commit 8673c7ec80... cvs2svn 21 years release/4.11.0 commit 7011c19a8c... cvs2svn 21 years release/5.3.0 commit 3add449a17... cvs2svn 21 years release/4.10.0 commit 6f316ce2e8... cvs2svn 22 years release/5.2.1 commit 00535086d3... cvs2svn 22 years release/5.2.0 commit a0848d7276... cvs2svn 22 years release/4.9.0 commit 1cf501d6f8... cvs2svn 22 years release/5.1.0 commit 6a294c2038... cvs2svn 23 years release/4.8.0 commit 2c36078d18... cvs2svn 23 years release/5.0.0 commit df3e7e93b8... cvs2svn 23 years release/4.7.0 commit 47ac56be82... cvs2svn 23 years release/4.6.2 commit f124555536... cvs2svn 23 years release/4.6.1 commit 306a059528... cvs2svn 23 years release/4.6.0 commit ec5d299eb6... cvs2svn 24 years release/4.5.0 commit b315e20b06... cvs2svn 24 years release/4.4.0 commit db3dca6dbd... cvs2svn 24 years release/4.3.0 commit 01a9f79f0a... cvs2svn 25 years release/4.2.0 commit d615f4ca56... cvs2svn 25 years release/4.1.1 commit 8c7499bdfa... cvs2svn 25 years release/4.1.0 commit 70f89778c0... cvs2svn 25 years release/3.5.0 commit e497906508... cvs2svn 26 years release/4.0.0 commit 6c59170bca... cvs2svn 26 years release/3.4.0 commit c9725fb85b... cvs2svn 26 years release/3.3.0 commit 0d24ce4729... cvs2svn 26 years release/3.2.0 commit b919b8311b... cvs2svn 27 years release/3.1.0 commit ce5da22b70... cvs2svn 27 years release/2.2.8 commit 4e2c51c2a5... cvs2svn 27 years release/3.0.0 commit 7e06d817b3... cvs2svn 27 years release/2.2.7 commit d250e9dd27... cvs2svn 27 years release/2.2.6 commit add94ad553... cvs2svn 28 years release/2.2.5 commit 056c3a9f3a... cvs2svn 28 years release/2.2.2 commit ed25ac248d... cvs2svn 29 years release/2.2.1 commit f2fd027c63... cvs2svn 29 years release/2.2.0 commit f2fd027c63... cvs2svn 29 years release/2.1.7 commit ff7652de1b... cvs2svn 29 years release/2.1.6 commit 519588bfa8... cvs2svn 29 years release/2.1.5 commit eadbcc5c79... cvs2svn 30 years release/2.1.0 commit 42499e5bc9... cvs2svn 30 years release/2.0.5 commit 444bcd3c2e... cvs2svn 31 years release/2.0.5a commit 0d98f12271... cvs2svn 31 years generated by cgit v1.2.3 ( git 2.25.1 ) at 2026-01-13 09:29:12 +0000 | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/how-twilio-built-a-multi-engine-query-platform-using-amazon-athena-and-open-source-presto/ | How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share Twilio is a customer engagement platform that powers real-time, personalized customer experiences for leading brands through APIs that democratize communications channels like voice, text, chat, and video. At Twilio, we manage a 20 petabyte-scale Amazon Simple Storage Service (Amazon S3) data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly, and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. A growing need for a multi-engine platform Our data platform has been built on Presto since its inception, but over the years as we expanded to support multiple business lines and diverse use cases, we began to encounter challenges related to scalability, operational overhead, and cost management. Maintaining the platform through frequent version upgrades also became difficult. These upgrades required significant time to evaluate backwards compatibility, integrate with our existing data ecosystem, and determine optimal configurations across releases. The administrative burden of upgrades and our commitment to minimizing user disruption caused our Presto version to fall behind. This prevented us from accessing the latest features and optimizations available in later releases. The adoption of Apache Hudi for our transaction-dependent critical workloads created a new requirement which our existing Presto deployment version couldnât support. We needed an up-to-date Presto or Trino compatible service to accommodate these use cases while still reducing the operational overhead of maintaining our own query infrastructure. Building a comprehensive data platform required us to balance multiple competing requirements and business constraints. We needed a solution that could support diverse workload types, from interactive analytics to ETL batch processing, while providing the flexibility to optimize compute resources based on specific use cases. We also wanted to improve upon cost management and attribution in our shared multi-tenanted query platform. Additionally, we needed to ensure that adopting any new technology did not cause any disruption to our users and maintained backward compatibility with existing systems during the transition period. Selecting Amazon Athena as our modern analytics engine Our users relied on SQL for interactive analysis, and we wanted to preserve this experience and make use of our existing jobs and application code. This meant we needed a Presto-compatible analytics service to modernize our data platform. Amazon Athena is a serverless interactive query service built on Presto and Trino that allows you to run queries using a familiar ANSI SQL interface. Athena appealed to us due to its compatibility with open-source Trino and its seamless upgrade experience. Athena helps to ease the burden of managing a large-scale query infrastructure, and with provisioned capacity , offers predictable and scalable pricing for our largest query workloads. Athenaâs workgroups provided the query and cost management capabilities we needed to efficiently support diverse teams and workload patterns with minimal overhead. The ability to blend on-demand and dedicated serverless capacity models allows us to optimize workload distribution for our requirements, achieving the flexibility and scalability needed in a managed query environment. To address latency-sensitive and predictive query workloads, we adopted provisioned capacity for its serverless capacity guarantee and workload concurrency control features. For queries that may be ad-hoc and more flexible in scheduling, we opted to use the cost-efficient multi-tenant on-demand model, which optimizes resource utilization through shared infrastructure. In parallel to migrating workloads to Athena, we also needed a way to support legacy workloads that use custom implementations of Presto features. This requirement drove us to abstract the underlying implementation, allowing us to present users with a unified interface. This would give us the flexibility key to future proof our infrastructure and use the most appropriate compute for the workload and use case. The birth of Odin The following diagram shows Twilioâs multi-engine query platform that incorporates both Amazon Athena and open-source Presto. High Level Architecture of Odinâs Query Engines Odin is a Presto-based gateway built on Zuul , an open-source L7 application gateway developed by Netflix. Zuul had already demonstrated its scalability at Twilio, having been successfully adopted by other internal teams. Since end users primarily connect to the platform via a JDBC connector using the Presto Driver (which operates through HTTP calls), Zuulâs specialization in HTTP call management made it an ideal technical choice for our needs. Odin functions as a central hub for query processing, employing a pluggable design that accommodates various query frameworks for maximum extensibility and flexibility. To interact with the Odin platform users are initially directed to an Amazon Application Load Balancer that sits in front of the Odin instances running on Amazon EC2. The Odin instances handle the authentication, routing, and entire query workflow throughout the queryâs lifetime. Amazon ElastiCache for Redis handles the query tracking for Athena and Amazon DynamoDB is responsible for the maintaining the query history. Both query engines, Amazon Athena and the Presto clusters running on Amazon EC2,are supported by the AWS Glue Data Catalog as the metastore repository and query data from our Amazon S3-based data lake. Routing queries to multiple engines We had a variety of use cases that were being served by this query platform and therefore we opted to use Amazon Athena as our primary query engine while continuing to route certain legacy workloads to our Presto clusters. Prior to our architectural redesign, we encountered operational challenges due to our end users being tightly bound to specific Presto clusters which led to inevitable disruptions during maintenance windows. Additionally, users frequently overloaded individual clusters with diverse workloads ranging from lightweight ad-hoc analytics to complex data warehousing queries and resource-intensive ETL processes. This prompted us to implement a more sophisticated routing solution, one that was use case focused and not tightly bound to the specific underlying compute. To enable routing across multiple query engines within the same platform, we developed a query hint mechanism that allows users to specify their intended use case. Users append this hint to the JDBC string via the X-Presto-Extra-Credential header , which Odinâs logical routing layer then evaluates alongside multiple factors including user identity, query origin, and fallback planning. The system also assesses whether the target resource has sufficient capacity, if not, it reroutes the query to an alternative resource with available capacity. While users provide initial context through their hints, Odin makes the final routing decisions intelligently on the server side. This approach balances user input with centralized orchestration, ensuring consistent performance and resource availability. For example, say a user might specify the following connection string when connecting to the Odin platform from a Tableau client: jdbc:presto://odin.twilio.com:443/hive?SSL=true&extraCredentials=routing:athena The connection string uses the extraCredentials header to signal execution on Athena, where Odin validates query submission details, including the submitting user and tool, before determining the appropriate Athena workgroup for initial routing. Since this Tableau data source and user qualify as âcritical queries,â the system routes them to a workgroup backed by capacity reservations. However, if that workgroup has too many pending queries in the execution queue, Odinâs routing logic automatically redirects to alternative workgroups with greater available resources. When necessary, queries may ultimately route to workgroups running on on-demand capacity. Through this fallback logic, Odin provides built-in load balancing at the routing layer, ensuring optimal utilization across the underlying compute infrastructure. Here is an example workflow of how our queries are routed to Athena workgroups: Once a query has been submitted to a workgroup for execution, Odin will also log the routing decision in our tracking system based on Amazon ElastiCache for Redis so that Odinâs routing logic can maintain real-time awareness of queue depths across all Athena workgroups. Additionally, Odin uses Amazon EventBridge to integrate with Amazon Athena to keep track of a query state change and create event-based workflows. Our Redis-based query tracking system effectively handles edge cases, such as when a JDBC client terminates mid-query. Even during such unexpected interruptions, the platform consistently maintains and updates the accurate state of the query. Query history Following successful query routing to either an Athena workgroup or one of our open-source Presto clusters, Odin persists the query identifier and destination endpoint in a query history table in DynamoDB. This design utilizes a RESTful architecture where initial query submissions operate as POST requests, while subsequent status checks function as GET requests that utilize DynamoDB as the authoritative lookup mechanism to locate and poll the appropriate execution engine. By centralizing query execution records in DynamoDB rather than maintaining state on individual servers, weâve created a truly stateless system where incoming requests can be handled by any Amazon EC2 instance hosting our Odin web service. Lessons learned The transition from open-source Presto to Athena required some adaptation time, due to subtle differences in how these query engines operate. Since our Odin framework was built on the Presto driver, we needed to modify our processing approach to ensure compatibility between both systems. As we began to adopt Athena for more use cases, we noticed a difference in the record counts between Athena and the original Presto queries. We discovered this was due to open-source Presto returning results with every page containing a header column, whereas Athena results only contain the header column on the first page and subsequent pages containing records only. This difference meant that for a 60-page result set, Athena would return 59 fewer rows than open-source Presto. Once we identified this pagination behavior, we optimized Odinâs result handling logic to properly interpret and process Athenaâs format, so that queries would return accurate results. Due to the nature of using the Odin platform, most of our interactions with the Athena service are API driven so we make use of the ResultSet object with the GetQueryResults API to retrieve query execution data. Using this mechanism, the API returns the data as all VARCHAR data type, even for complex types such as row, map, or array. This created a challenge because Odin uses the Presto driver for query parsing, resulting in a type mismatch between the expected formats and actual returned data. To address this, we implemented a translation layer within the Odin framework that converts all data types to VARCHAR and handles any downstream implications of this conversion internally. These technical adjustments, while initially challenging, highlighted the importance of carefully managing the subtle differences between different query execution engines when building a unified data platform. Scale of Odin and looking ahead The Odin platform serves over 1,500 users who execute approximately 80,000 queries daily, totaling 2.5 million queries per month. Odin also powers more than 5,000 Business Intelligence (BI) reports and dashboards for Tableau and Looker. The queries are executed across our multi-engine landscape of more than 30 workgroups in Athena based on both provisioned capacity and on-demand workgroups and 4 Presto clusters on running on EC2 instances with Auto Scaling enabled that run on average 180 instances each. As Twilio continues to experience rapid growth, our Odin platform has enabled us to mature our technology stacks by both upgrading existing compute resources and integrating new technologies. We can do all this without disrupting the experience for our end users. While Odin serves as our foundation, weâre excited to continue to expand this pluggable infrastructure. Our roadmap includes migrating our self-managed open-source Presto implementation to EMR Trino, introducing Apache Spark as a compute engine via Amazon EMR Serverless or AWS Glue jobs, and integrating generative AI capabilities to intelligently route queries across Odinâs various compute options. Conclusion In this post, weâve shared how we built Odin, our unified multi-engine query platform. By combining AWS services like Amazon Athena, Amazon ElastiCache for Redis, and Amazon DynamoDB with our open-source technology stack, we created a transparent abstraction layer for users. This integration has resulted in a highly available and resilient platform environment that serves our query processing needs. By embracing this multi-engine approach, not only did we solve our query infrastructure challenges but we also established a flexible foundation that will continue to evolve with our data needs, ensuring we can deliver powerful insights at scale regardless of how technology trends shift in the future. To learn more and get started using Amazon Athena, please see the Athena User Guide . About the authors Aakash Pradeep Aakash is a Senior Software Engineer at Adobe with over 15 years of experience across ingestion, compute, storage, and query platforms. Previously, at Twilio, he worked extensively on developing the Odin platform to serve his customers query infrastructure needs. Aakash is a PrestoCon speaker, holds multiple patents in real-time analytics, and is passionate about building high-performance distributed systems. Venkatram Bondugula Venkatram is a seasoned backend engineer with over a decade of experience specializing in the design and development of scalable data platforms for big data and distributed systems. With a strong background in backend architecture and data engineering, he has built and optimized high-performance systems that power data-driven decision-making at scale. Amber Runnels Amber is a Senior Analytics Specialist Solutions Architect at AWS specializing in big data and distributed systems. She helps customers optimize workloads in the AWS data ecosystem to achieve a scalable, performant, and cost-effective architecture. Aside from technology, she is passionate about exploring the many places and cultures this world has to offer, reading novels, and building terrariums. 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https://aws.amazon.com/blogs/big-data/accelerate-analytics-and-ai-innovation-with-the-next-generation-of-amazon-sagemaker/ | Accelerate analytics and AI innovation with the next generation of Amazon SageMaker | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Accelerate analytics and AI innovation with the next generation of Amazon SageMaker by G2 Krishnamoorthy and Rahul Pathak on 13 MAR 2025 in Amazon SageMaker , Amazon SageMaker Unified Studio , Analytics , Artificial Intelligence , Thought Leadership Permalink Comments Share At AWS re:Invent 2024, we announced the next generation of Amazon SageMaker , the center for all your data, analytics, and AI. Amazon SageMaker brings together widely adopted AWS machine learning (ML) and analytics capabilities and addresses the challenges of harnessing organizational data for analytics and AI through unified access to tools and data with governance built in. It enables teams to securely find, prepare, and collaborate on data assets and build analytics and AI applications through a single experience, accelerating the path from data to value. At the core of the next generation of Amazon SageMaker is Amazon SageMaker Unified Studio , a single data and AI development environment where you can find and access your organizationâs data and act on it using the best tool for the job across virtually any use case. We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started. Amazon SageMaker Unified Studio SageMaker Unified Studio brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR , AWS Glue , Amazon Athena , Amazon Redshift , Amazon Bedrock , and Amazon SageMaker AI . From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications. Governance features including fine-grained access control are built into SageMaker Unified Studio using Amazon SageMaker Catalog to help you meet enterprise security requirements across your entire data estate. Unified access to your data is provided by Amazon SageMaker Lakehouse , a unified, open, and secure data lakehouse built on Apache Iceberg open standards. Whether your data is stored in Amazon Simple Storage Service (Amazon S3) data lakes, Redshift data warehouses, or third-party and federated data sources, you can access it from one place and use it with Iceberg-compatible engines and tools. In addition, SageMaker Lakehouse now integrates with Amazon S3 Tables , the first cloud object store with native Apache Iceberg support, so you can use SageMaker Lakehouse to create, query, and process S3 Tables efficiently using various analytics engines in SageMaker Unified Studio as well as Iceberg-compatible engines like Apache Spark and PyIceberg. Capabilities from Amazon Bedrock are now generally available in SageMaker Unified Studio, allowing you to rapidly prototype, customize, and share generative AI applications in a governed environment. Users have an intuitive interface to access high-performing foundation models (FMs) in Amazon Bedrock, including the Amazon Nova model series, and the ability to create Agents, Flows, Knowledge Bases, and Guardrails with a few clicks. Amazon Q Developer , the most capable generative AI assistant for software development, can be used within SageMaker Unified Studio to streamline tasks across the data and AI development lifecycle, including code authoring, SQL generation, data discovery, and troubleshooting. A new integrated way of working The general availability of SageMaker Unified Studio represents another meaningful step in our journey to offer our customers a streamlined way to work with their data, whether for analytics or AI. Many of our customers have told us that you are building data-driven applications to guide business decisions, improve agility, and drive innovation, but that these applications are complex to build because they require collaboration across teams and the integration of data and tools. Not only is it time consuming for users to learn multiple development experiences, but because data, code, and other development artifacts are stored separately, it is challenging for users to understand how they interact with each other and to use them cohesively. Configuring and governing access is also a cumbersome manual process. To overcome these hurdles, many organizations are building bespoke integrations between services, tools, and homegrown access management systems. However, what you need is the flexibility to adopt the best services for your use case while empowering your data teams with a unified development experience. âAt Carrier, the next generation of Amazon SageMaker is transforming our enterprise data strategy by streamlining how we build and scale data products. SageMaker Unified Studioâs approach to data discovery, processing, and model development has significantly accelerated our lakehouse implementation. Most impressively, its seamless integration with our existing data catalog and built-in governance controls enables us to democratize data access while maintaining security standards, helping our teams rapidly deliver advanced analytics and AI solutions across the enterprise.â â Justin McDowell, Director Data Platform & Data Engineering, Carrier Millions of organizations trust AWS and utilize our comprehensive set of purpose-built analytics, AI/ML, and generative AI capabilities to power data-driven applications without compromising on performance, scale, or cost. Our goal for the next generation of Amazon SageMaker, including SageMaker Unified Studio, is to make data and AI workers more productive by providing access to all your data and tools in a single development environment. Building from a single data and AI development environment Letâs explore a common business challenge: increasing revenue through better lead generation. Consider an organization implementing an intelligent digital assistant on their website to engage with customersâa process that traditionally requires multiple tools and data sources. With SageMaker Unified Studio, this entire process can now be carried out within a single data and AI development environment. First, the data team uses the generative AI playground within SageMaker Unified Studio to quickly evaluate and select the best model for their customer interactions. They then create a project to house the tools and resources necessary for their use case and use Amazon Bedrock within the project to build and deploy a sophisticated virtual assistant that quickly begins qualifying leads through their website. To identify the most promising opportunities, the team develops a segmentation strategy. The data engineer asks Amazon Q Developer to identify datasets that contain lead data and uses zero-ETL integrations to bring the data into SageMaker Lakehouse. The data analyst then discovers it and creates a comprehensive view of their market. They use the SQL query editor to build out marketing segments, which they then write back to SageMaker Lakehouse, where they are available to other team members. Finally, the data scientist accesses the same dataset, which they use to train and deploy an automated lead scoring model using tools available from SageMaker AI. During the model development phase, they use Amazon Q Developerâs inline code authoring and troubleshooting capabilities to efficiently write error free-code in their JupyterLab notebook. The final model provides sales teams with the highest-value opportunities, which they can visualize in a business intelligence dashboard and take action on immediately. Reducing time-to-value in a unified environment What is remarkable about this example is that entire process happens in one integrated environment. Without SageMaker Unified Studio, the team would have had to work with multiple data sources, tools, and services, spending time learning multiple development environments, creating resources shares, and manually configuring access controls. The data engineer and data analyst would have worked in various data warehouses, data lakes, and analytics tools, the data scientist would have worked in an ML studio and notebook environment, and the application builder in a generative AI tool. Now, theyâre able to build and collaborate with their data and tools available in one experience, dramatically reducing time-to-value. Thatâs why weâre so excited about the next generation of Amazon SageMaker and the general availability of SageMaker Unified Studio. We believe that by putting everything you need for analytics and AI in one place, you can solve complex end-to-end problems more efficiently and get to innovative outcomes faster than ever before. Getting started with SageMaker Unified Studio To learn more, check out the following resources: Amazon SageMaker Amazon SageMaker Unified Studio Amazon SageMaker Lakehouse About the authors G2 Krishnamoorthy is VP of Analytics, leading AWS data lake services, data integration, Amazon OpenSearch Service, and Amazon QuickSight. Prior to his current role, G2 built and ran the Analytics and ML Platform at Facebook/Meta, and built various parts of the SQL Server database, Azure Analytics, and Azure ML at Microsoft. Rahul Pathak is VP of Relational Database Engines, leading Amazon Aurora, Amazon Redshift, and Amazon QLDB. Prior to his current role, he was VP of Analytics at AWS, where he worked across the entire AWS database portfolio. He has co-founded two companies, one focused on digital media analytics and the other on IP-geolocation. 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https://aws.amazon.com/blogs/big-data/category/artificial-intelligence/sagemaker/ | Amazon SageMaker | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon SageMaker Access Databricks Unity Catalog data using catalog federation in the AWS Glue Data Catalog by Srividya Parthasarathy and Venkat Viswanathan on 12 JAN 2026 in Advanced (300) , Amazon SageMaker , AWS Glue , AWS Lake Formation , Technical How-to Permalink Comments Share AWS has launched the catalog federation capability, enabling direct access to Apache Iceberg tables managed in Databricks Unity Catalog through the AWS Glue Data Catalog. With this integration, you can discover and query Unity Catalog data in Iceberg format using an Iceberg REST API endpoint, while maintaining granular access controls through AWS Lake Formation. In this post, we demonstrate how to set up catalog federation between the Glue Data Catalog and Databricks Unity Catalog, enabling data querying using AWS analytics services. Use Amazon SageMaker custom tags for project resource governance and cost tracking by David Victoria , Ahan Malli , and Rohit Srikanta on 08 JAN 2026 in Advanced (300) , Amazon SageMaker , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share Amazon SageMaker announced a new feature that you can use to add custom tags to resources created through an Amazon SageMaker Unified Studio project. This helps you enforce tagging standards that conform to your organizationâs service control policies (SCPs) and helps enable cost tracking reporting practices on resources created across the organization. In this post, we look at use cases for custom tags and how to use the AWS Command Line Interface (AWS CLI) to add tags to project resources. Introducing catalog federation for Apache Iceberg tables in the AWS Glue Data Catalog by Debika D , Pratik Das , and Srividya Parthasarathy on 26 NOV 2025 in Advanced (300) , Amazon SageMaker , Announcements , AWS Glue , AWS Lake Formation Permalink Comments Share AWS Glue now supports catalog federation for remote Iceberg tables in the Data Catalog. With catalog federation, you can query remote Iceberg tables, stored in Amazon S3 and cataloged in remote Iceberg catalogs, using AWS analytics engines and without moving or duplicating tables. In this post, we discuss how to get started with catalog federation for Iceberg tables in the Data Catalog. Accelerate data lake operations with Apache Iceberg V3 deletion vectors and row lineage by Ron Ortloff on 26 NOV 2025 in Amazon EMR , Amazon SageMaker , Amazon Simple Storage Service (S3) , Announcements , AWS Glue , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services. Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Optimize efficiency with language analyzers using scalable multilingual search in Amazon OpenSearch Service by Sunil Ramachandra , Mingshi Liu , and Sampath Kathirvel on 02 OCT 2025 in Amazon OpenSearch Service , Amazon SageMaker Studio , Technical How-to Permalink Comments Share Organizations manage content across multiple languages as they expand globally. Ecommerce platforms, customer support systems, and knowledge bases require efficient multilingual search capabilities to serve diverse user bases effectively. This unified search approach helps multinational organizations maintain centralized content repositories while making sure users, regardless of their preferred language, can effectively find and access relevant [âŠ] Accelerate your data and AI workflows by connecting to Amazon SageMaker Unified Studio from Visual Studio Code by Lauren Mullennex , Anagha Barve , Anchit Gupta , and Bhargava Varadharajan on 12 SEP 2025 in Amazon SageMaker AI , Amazon SageMaker Unified Studio , Announcements , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we demonstrate how to connect your local VS Code to SageMaker Unified Studio so you can build complete end-to-end data and AI workflows while working in your preferred development environment. The Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control for federated catalogs by Sandeep Adwankar , Aarthi Srinivasan , and Srividya Parthasarathy on 29 AUG 2025 in Amazon SageMaker , Amazon SageMaker Lakehouse , Analytics , Announcements , AWS Big Data , Intermediate (200) Permalink Comments Share We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless. Amazon SageMaker Catalog expands discoverability and governance for Amazon S3 general purpose buckets by Priya Tiruthani , Yuhang Huang , Santhosh Padmanabhan , and Subrat Das on 21 AUG 2025 in Amazon SageMaker , Announcements , Intermediate (200) Permalink Comments Share In July 2025, Amazon SageMaker announced support for Amazon Simple Storage Service (Amazon S3) general purpose buckets and prefixes in Amazon SageMaker Catalog that delivers fine-grained access control and permissions through S3 Access Grants. In this post, we explore how this integration addresses key challenges our customers have shared with us, and how data producers, such as administrators and data engineers, can seamlessly share and govern S3 buckets and prefixes using S3 Access Grants, while making it readily discoverable for data consumers. Guide to adopting Amazon SageMaker Unified Studio from ATPCOâs Journey by Mitesh Patel , Nikki Rouda , Raj Samineni , and Saurabh Rawat on 18 AUG 2025 in Advanced (300) , Amazon SageMaker , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio. â Older posts Create an AWS account Learn What Is AWS? <a data-rg-n="Link" href="/what-is-cloud-computing/?nc1=f_cc" data-rigel-analytics="{"name":"Link","properties":{"size":1}}" class="rgft_8711ccd9 rgft_ | 2026-01-13T09:29:12 |
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https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/sys/conf/#dirlist | src/sys/conf/ src/sys/conf/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src / sys / conf File Rev. Age Author Last log entry Parent Directory GENERIC 1.304 3 months jsg re-enable POOL_DEBUG ok deraadt@ files 1.749 28 hours dlg remove lacp support from trunk(4) lacp is better supported by aggr(4) . users of... makegap.sh 1.14 7 years mpi Use a new LDFLAGS variable to pass "-melf_i386_obsd" on amd64. Makes the linker... newvers.sh 1.213 3 months jsg 7.8-current ok deraadt@ param.c 1.53 5 months mvs Unlock the KERN_MAXCLUSTERS case of kern_sysctl(). The `nmbclust' and `mbuf_mem... swapgeneric.c 1.6 14 months jsg remove unneeded conf.h include defines (in the Attic) [Hide] 1.5 3 months jsg remove sys/conf/defines used by links target which was removed from conf/Makefi... files.oldconf (in the Attic) [Hide] 1.18 28 years downsj Obsolete. Show only files with tag: All tags / default branch UBC_SYNC_B UBC_SYNC_A UBC_BASE UBC SMP_SYNC_B SMP_SYNC_A SMP_BASE SMP OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN kame_19991208 HEAD Module path or alias: | 2026-01-13T09:29:12 |
https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/sys/#dirlist | src/sys/ src/sys/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src / sys File Rev. Age Author Last log entry Parent Directory adosfs/ altq/ arch/ compat/ conf/ crypto/ ddb/ dev/ gnu/ isofs/ kern/ lib/ miscfs/ msdosfs/ net/ net80211/ netatalk/ netbt/ netccitt/ netinet/ netinet6/ netipx/ netiso/ netmpls/ netnatm/ netns/ nfs/ nnpfs/ ntfs/ scsi/ stand/ sys/ tmpfs/ ufs/ uvm/ vm/ xfs/ .gitignore 1.3 2 years bluhm Bring src/sys/.gitignore in sync with src/.gitignore. OK tobhe@ Makefile 1.53 4 years visa Retire OpenBSD/sgi. OK deraadt@ README.SMP (in the Attic) [Hide] 1.1 24 years niklas branches: 1.1.2; file README.SMP was initially added on branch SMP. Show only files with tag: All tags / default branch UBC_SYNC_B UBC_SYNC_A UBC_BASE UBC SMP_SYNC_B SMP_SYNC_A SMP_BASE SMP OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN kame_19991208 HEAD Module path or alias: | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/analytics/amazon-sagemaker-data-ai-governance/ | Amazon SageMaker Data & AI Governance | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon SageMaker Data & AI Governance Navigating architectural choices for a lakehouse using Amazon SageMaker by Lakshmi Nair and Saman Irfan on 12 JAN 2026 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics Permalink Comments Share Over time, several distinct lakehouse approaches have emerged. In this post, we show you how to evaluate and choose the right lakehouse pattern for your needs. A lakehouse architecture isnât about choosing between a data lake and a data warehouse. Instead, itâs an approach to interoperability where both frameworks coexist and serve different purposes within a unified data architecture. By understanding fundamental storage patterns, implementing effective catalog strategies, and using native storage capabilities, you can build scalable, high-performance data architectures that support both your current analytics needs and future innovation. AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale by Larry Weber on 07 JAN 2026 in Amazon EMR , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon OpenSearch Service , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , AWS Glue , AWS Lake Formation , AWS re:Invent , Intermediate (200) Permalink Comments Share re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, [âŠ] Reference guide for building a self-service analytics solution with Amazon SageMaker by Navnit Shukla , Ayan Majumder , and Karan Edikala on 16 DEC 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Technical How-to Permalink Comments Share In this post, we show how to use Amazon SageMaker Catalog to publish data from multiple sources, including Amazon S3, Amazon Redshift, and Snowflake. This approach enables self-service access while ensuring robust data governance and metadata management. How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker by Avinash Erupaka , Radhika Kashyap , and Modood Alvi on 12 DEC 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows. Enforce business glossary classification rules in Amazon SageMaker Catalog by Ramesh H Singh , Manny Pelaez , Pradeep Misra , and Pradyut Singh on 20 NOV 2025 in Amazon SageMaker Data & AI Governance , Announcements , Intermediate (200) Permalink Comments Share Amazon SageMaker Catalog now supports metadata enforcement rules for glossary terms classification (tagging) at the asset level. With this capability, administrators can require that assets include specific business terms or classifications. Data producers must apply required glossary terms or classifications before an asset can be published. In this post, we show how to enforce business glossary classification rules in SageMaker Catalog. Enhanced data discovery in Amazon SageMaker Catalog with custom metadata forms and rich text documentation by Ramesh H Singh , Abbas Makhdum , Harish Panwar , and Pradeep Misra on 20 NOV 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Announcements Permalink Comments Share Amazon SageMaker Catalog now supports custom metadata forms and rich text descriptions at the column level, extending existing curation capabilities for business names, descriptions, and glossary term classifications. Column-level context is essential for understanding and trusting data. This release helps organizations improve data discoverability, collaboration, and governance by letting metadata stewards document columns using structured and formatted information that aligns with internal standards. In this post, we show how to enhance data discovery in SageMaker Catalog with custom metadata forms and rich text documentation at the schema level. Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Accelerate data governance with custom subscription workflows in Amazon SageMaker by Nira Jaiswal and Ajit Tandale on 24 OCT 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share Organizations need to efficiently manage data assets while maintaining governance controls in their data marketplaces. Although manual approval workflows remain important for sensitive datasets and production systems, thereâs an increasing need for automated approval processes with less sensitive datasets. In this post, we show you how to automate subscription request approvals within SageMaker, accelerating data access for data consumers. Automate email notifications for governance teams working with Amazon SageMaker Catalog by Himanshu Sahni , Jitesh Kumar , and Rajiv Upadhyay on 21 OCT 2025 in Advanced (300) , Amazon DataZone , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, we show you how to create custom notifications for events occurring in SageMaker Catalog using Amazon EventBridge, AWS Lambda, and Amazon SNS. You can expand this solution to automatically integrate SageMaker Catalog with in-house enterprise workflow tools like ServiceNow and Helix. Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift by Shubham Purwar , Nitin Kumar , and Prashanthi Chinthala on 13 OCT 2025 in Amazon Athena , Amazon EMR , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , AWS Glue , Expert (400) , Technical How-to Permalink Comments Share Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including [âŠ] â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? <a data-rg-n="Link" href="/what-is/?nc1=f_cc" data-rigel-analytics="{"name":"Link","properties":{"size":1}}" cla | 2026-01-13T09:29:12 |
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https://aws.amazon.com/blogs/big-data/medidatas-journey-to-a-modern-lakehouse-architecture-on-aws/ | Medidataâs journey to a modern lakehouse architecture on AWS | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Medidataâs journey to a modern lakehouse architecture on AWS by Mike Araujo , Ashley Chen , Ian Beatty , and Sandeep Adwankar on 26 NOV 2025 in Amazon Kinesis , Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , Case Study , Customer Solutions Permalink Comments Share This post was co-authored by Mike Araujo Principal Engineer at Medidata Solutions. The life sciences industry is transitioning from fragmented, standalone tools towards integrated, platform-based solutions. Medidata , a Dassault SystÚmes company, is building a next-generation data platform that addresses the complex challenges of modern clinical research. In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg , and a modern lakehouse architecture. Challenges with legacy architecture As the Medidata portfolio of data offerings expanded and our strategy evolved to offering an end-to-end clinical data platform experience, the team recognized the need to evolve the data platform architecture. The following diagram shows Medidataâs legacy extract, transform, and load (ETL) architecture. The legacy system was built on scheduled batch jobs, which served Medidata well for a long time, but wasnât designed for a unified view of data across a growing ecosystem. Batch jobs ran at different intervals, requiring scheduling buffers to make sure upstream jobs completed on time. As data volume grew, these schedules became more complex, introducing latency between ingestion and processing. Different consumers pulling from various data services also meant teams were continuously building new pipelines for each delivery stack. As the number of pipelines grew, so did the maintenance burden. More operations meant more potential points of failure and more complex recovery. The observability systems were handling a growing volume of operational data, making root cause analysis more involved. Scaling for increased data volume required coordinated changes across all our systems. Additionally, having data pipelines and copies spread across different technologies and storage systems meant access controls had to be managed in multiple places. Propagating access control changes correctly across all systems added complexity for both consumers and producers. Solution overview With the advent of Clinical Data Studio (Medidataâs unified data management and analytics solution for clinical trials) and Data Connect (Medidataâs data solution for acquiring, transforming, and exchanging electronic health record (EHR) data across healthcare organizations), Medidata introduced a new world of data discovery, analysis, and integration to the life sciences industry powered by open source technologies and hosted on AWS. The following diagram illustrates the solution architecture. Fragmented batch ETL jobs were replaced by real-time Apache Flink streaming pipelines, an open source, distributed engine for stateful processing, and powered by Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed Kubernetes service. The Flink jobs write to Apache Kafka running in Amazon Managed Apache Kafka (Amazon MSK), a streaming data service that manages Kafka infrastructure and operations, before landing in Iceberg tables backed by the AWS Glue Data Catalog , a centralized metadata repository for data assets. From this collection of Iceberg tables, a central, single source of data is now accessible from a variety of consumers without additional downstream processing, alleviating the need for custom pipelines to satisfy the requirements of downstream consumers. Through these fundamental architectural changes, the team at Medidata solved the issues presented by the legacy solution. Data availability and consistency With the introduction of the Flink jobs and Iceberg tables, the team was able to deliver a consistent view of their data across the Medidata data experience. Pipeline latency was reduced from numerous hours and sometimes days to minutes, helping Medidata customers realize a 99% performance gain from the data ingestion to the data analytics layers. Due to Icebergâs interoperability, Medidata users saw the same view of the data regardless of where they viewed that data, minimizing the need for consumer-driven custom pipelines because Iceberg could plug into existing consumers. Maintenance and durability Icebergâs interoperability provided a single copy of the data to satisfy their use cases, so the Medidata team could focus its observation and maintenance efforts on a five-times smaller subset of operations than previously required. Observability was enhanced by tapping into the various metadata components and metrics exposed by Iceberg and the Data Catalog. Quality management transformed from cross-system traces and queries to a single analysis of unified pipelines, with an added benefit of point in time data queries thanks to the Iceberg snapshot feature . Data volume increases are handled with out-of-box scaling supported by the entire infrastructure stack and AWS Glue Iceberg optimization features that include compaction , snapshot retention , and orphan file deletion , which provide a set-and-forget experience for solving a number of common Iceberg frustrations, such as the small file problem, orphan file retention, and query performance. Security With Iceberg at the center of its solution architecture, the Medidata team no longer had to spend the time building custom access control layers with enhanced security features at each data integration point. Iceberg on AWS centralizes the authorization layer using familiar systems such as AWS Identity and Access Management (IAM), providing a single and durable control for data access. The data also stays entirely within the Medidata virtual private cloud (VPC), further reducing the opportunity for unintended disclosures. Conclusion In this post, we demonstrated how legacy universe of consumer-driven custom ETL pipelines can be replaced with a scalable, high-performant streaming lakehouses. By putting Iceberg on AWS at the center of data operations, you can have a single source of data for your consumers. To learn more about Iceberg on AWS, refer to Optimizing Iceberg tables and Using Apache Iceberg on AWS . About the authors Mike Araujo Mike is a Principal Engineer at Medidata Solutions, working on building a next generation data and AI platform for clinical data and trials. By using the power of open source technologies such as Apache Kafka, Apache Flink, and Apache Iceberg, Mike and his team have enabled the delivery of billions of clinical events and data transformations in near real time to downstream consumers, applications, and AI agents. His core skills focus on architecting and building big data and ETL solutions at scale as well as their integration in agentic workflows. Sandeep Adwankar Sandeep is a Senior Product Manager at AWS, who has driven feature launches across Amazon SageMaker, AWS Glue, and AWS Lake Formation. He has led initiatives in Amazon S3 Tables analytics, Iceberg compaction strategies, and AWS Glue Iceberg optimizations. His recent work focuses on generative AI and autonomous systems, including the AWS Glue Data Catalog model context protocol and Amazon Bedrock structured knowledge bases. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that accelerate their business outcomes. Ian Beatty Ian is a Technical Account Manager at AWS, where he specializes in supporting independent software vendor (ISV) customers in the healthcare and life sciences (HCLS) and financial services industry (FSI) sectors. Based in the Rochester, NY area, Ian helps ISV customers navigate their cloud journey by maintaining resilient and optimized workloads on AWS. With over a decade of experience building on AWS since 2014, he brings deep technical expertise from his previous roles as an AWS Architect and DevSecOps team lead for SaaS ISVs before joining AWS more than 3 years ago. Ashley Chen Ashley is a Solutions Architect at AWS based in Washington D.C. She supports independent software vendor (ISV) customers in the healthcare and life sciences industries, focusing on customer enablement, generative AI applications, and container workloads. Loading comments⊠Resources Amazon Athena Amazon EMR Amazon Kinesis Amazon MSK Amazon QuickSight Amazon Redshift AWS Glue Follow Twitter Facebook LinkedIn Twitch Email Updates Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? 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https://aws.amazon.com/blogs/big-data/category/analytics/amazon-redshift-analytics/ | Amazon Redshift | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon Redshift AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale by Larry Weber on 07 JAN 2026 in Amazon EMR , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon OpenSearch Service , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , AWS Glue , AWS Lake Formation , AWS re:Invent , Intermediate (200) Permalink Comments Share re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, [âŠ] Simplify multi-warehouse data governance with Amazon Redshift federated permissions by Satesh Sonti , Ning Di , Sandeep Adwankar , Ramchandra Anil Kulkarni , and Abhishek Rai Sharma on 05 JAN 2026 in Advanced (300) , Amazon Redshift , Technical How-to Permalink Comments Share Amazon Redshift federated permissions simplify permissions management across multiple Redshift warehouses. In this post, we show you how to define data permissions one time and automatically enforce them across warehouses in your AWS account, removing the need to re-create security policies in each warehouse. Best practices for querying Apache Iceberg data with Amazon Redshift by Anusha Challa , Jonathan Katz , and Mohammed Alkateb on 17 DEC 2025 in Amazon Redshift , Amazon S3 Tables , Best Practices Permalink Comments Share In this post, we discuss the best practices that you can follow while querying Apache Iceberg data with Amazon Redshift IPv6 addressing with Amazon Redshift by Srini Ponnada , Zirui Hua , Niranjan Kulkarni , Sandeep Adwankar , Sumanth Punyamurthula , and Yanzhu Ji on 17 DEC 2025 in Advanced (300) , Amazon Redshift , Technical How-to Permalink Comments Share As we witness the gradual transition from IPv4 to IPv6, AWS continues to expand its support for dual-stack networking across its service portfolio. In this post, we show how you can migrate your Amazon Redshift Serverless workgroup from IPv4-only to dual-stack mode, so you can make your data warehouse future ready. Achieve 2x faster data lake query performance with Apache Iceberg on Amazon Redshift by Kalaiselvi Kamaraj , Aamer Shah , Fabian Nagel , Ravi Animi , and Stefan Gromoll on 26 NOV 2025 in Amazon Redshift , Announcements , Intermediate (200) Permalink Comments Share In 2025, Amazon Redshift delivered several performance optimizations that improved query performance over twofold for Iceberg workloads on Amazon Redshift Serverless, delivering exceptional performance and cost-effectiveness for your data lake workloads. In this post, we describe some of the optimizations that led to these performance gains. Getting started with Apache Iceberg write support in Amazon Redshift by Sanket Hase , Harshida Patel , Ritesh Sinha , Raghu Kuppala , and Xiening Dai on 26 NOV 2025 in Advanced (300) , Amazon Redshift , Amazon S3 Tables , Technical How-to Permalink Comments Share In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance. Save up to 24% on Amazon Redshift Serverless compute costs with Reservations by Satesh Sonti and Ashish Agrawal on 24 NOV 2025 in Advanced (300) , Amazon Redshift , Best Practices , Cloud Cost Optimization , Serverless Permalink Comments Share In this post, you learn how Amazon Redshift Serverless Reservations can help you lower your data warehouse costs. We explore ways to determine the optimal number of RPUs to reserve, review example scenarios, and discuss important considerations when purchasing these reservations. Upgrade from Amazon Redshift DC2 node type to Amazon Redshift Serverless by Nita Shah , Bryan Cottle , Semir Naffati , and Ricardo Serafim on 22 OCT 2025 in Advanced (300) , Amazon Redshift , Technical How-to Permalink Comments Share In this post, we show you the upgrade process from DC2 instances to Amazon Redshift Serverless. By using Amazon Redshift Serverless, you can run and scale analytics without managing data warehouse infrastructure. Best practices for upgrading from Amazon Redshift DC2 to RA3 and Amazon Redshift Serverless by Ziad Wali , Omama Khurshid , and Srikant Das on 15 OCT 2025 in Advanced (300) , Amazon Redshift , Analytics , Best Practices Permalink Comments Share As analytical demands grow, many customers are upgrading from DC2 to RA3 or Amazon Redshift Serverless, which offer independent compute and storage scaling, along with advanced capabilities such as data sharing, zero-ETL integration, and built-in artificial intelligence and machine learning (AI/ML) support with Amazon Redshift ML. This post provides a practical guide to plan your target architecture and migration strategy, covering upgrade options, key considerations, and best practices to facilitate a successful and seamless transition. Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift by Shubham Purwar , Nitin Kumar , and Prashanthi Chinthala on 13 OCT 2025 in Amazon Athena , Amazon EMR , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , AWS Glue , Expert (400) , Technical How-to Permalink Comments Share Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including [âŠ] â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases Resources Getting Started Training AWS Trust Center AWS Solutions Library Architecture Center Product and Technical FAQs Analyst Reports AWS Pa | 2026-01-13T09:29:12 |
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https://aws.amazon.com/blogs/big-data/how-twilio-built-a-multi-engine-query-platform-using-amazon-athena-and-open-source-presto/ | How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share Twilio is a customer engagement platform that powers real-time, personalized customer experiences for leading brands through APIs that democratize communications channels like voice, text, chat, and video. At Twilio, we manage a 20 petabyte-scale Amazon Simple Storage Service (Amazon S3) data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly, and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. A growing need for a multi-engine platform Our data platform has been built on Presto since its inception, but over the years as we expanded to support multiple business lines and diverse use cases, we began to encounter challenges related to scalability, operational overhead, and cost management. Maintaining the platform through frequent version upgrades also became difficult. These upgrades required significant time to evaluate backwards compatibility, integrate with our existing data ecosystem, and determine optimal configurations across releases. The administrative burden of upgrades and our commitment to minimizing user disruption caused our Presto version to fall behind. This prevented us from accessing the latest features and optimizations available in later releases. The adoption of Apache Hudi for our transaction-dependent critical workloads created a new requirement which our existing Presto deployment version couldnât support. We needed an up-to-date Presto or Trino compatible service to accommodate these use cases while still reducing the operational overhead of maintaining our own query infrastructure. Building a comprehensive data platform required us to balance multiple competing requirements and business constraints. We needed a solution that could support diverse workload types, from interactive analytics to ETL batch processing, while providing the flexibility to optimize compute resources based on specific use cases. We also wanted to improve upon cost management and attribution in our shared multi-tenanted query platform. Additionally, we needed to ensure that adopting any new technology did not cause any disruption to our users and maintained backward compatibility with existing systems during the transition period. Selecting Amazon Athena as our modern analytics engine Our users relied on SQL for interactive analysis, and we wanted to preserve this experience and make use of our existing jobs and application code. This meant we needed a Presto-compatible analytics service to modernize our data platform. Amazon Athena is a serverless interactive query service built on Presto and Trino that allows you to run queries using a familiar ANSI SQL interface. Athena appealed to us due to its compatibility with open-source Trino and its seamless upgrade experience. Athena helps to ease the burden of managing a large-scale query infrastructure, and with provisioned capacity , offers predictable and scalable pricing for our largest query workloads. Athenaâs workgroups provided the query and cost management capabilities we needed to efficiently support diverse teams and workload patterns with minimal overhead. The ability to blend on-demand and dedicated serverless capacity models allows us to optimize workload distribution for our requirements, achieving the flexibility and scalability needed in a managed query environment. To address latency-sensitive and predictive query workloads, we adopted provisioned capacity for its serverless capacity guarantee and workload concurrency control features. For queries that may be ad-hoc and more flexible in scheduling, we opted to use the cost-efficient multi-tenant on-demand model, which optimizes resource utilization through shared infrastructure. In parallel to migrating workloads to Athena, we also needed a way to support legacy workloads that use custom implementations of Presto features. This requirement drove us to abstract the underlying implementation, allowing us to present users with a unified interface. This would give us the flexibility key to future proof our infrastructure and use the most appropriate compute for the workload and use case. The birth of Odin The following diagram shows Twilioâs multi-engine query platform that incorporates both Amazon Athena and open-source Presto. High Level Architecture of Odinâs Query Engines Odin is a Presto-based gateway built on Zuul , an open-source L7 application gateway developed by Netflix. Zuul had already demonstrated its scalability at Twilio, having been successfully adopted by other internal teams. Since end users primarily connect to the platform via a JDBC connector using the Presto Driver (which operates through HTTP calls), Zuulâs specialization in HTTP call management made it an ideal technical choice for our needs. Odin functions as a central hub for query processing, employing a pluggable design that accommodates various query frameworks for maximum extensibility and flexibility. To interact with the Odin platform users are initially directed to an Amazon Application Load Balancer that sits in front of the Odin instances running on Amazon EC2. The Odin instances handle the authentication, routing, and entire query workflow throughout the queryâs lifetime. Amazon ElastiCache for Redis handles the query tracking for Athena and Amazon DynamoDB is responsible for the maintaining the query history. Both query engines, Amazon Athena and the Presto clusters running on Amazon EC2,are supported by the AWS Glue Data Catalog as the metastore repository and query data from our Amazon S3-based data lake. Routing queries to multiple engines We had a variety of use cases that were being served by this query platform and therefore we opted to use Amazon Athena as our primary query engine while continuing to route certain legacy workloads to our Presto clusters. Prior to our architectural redesign, we encountered operational challenges due to our end users being tightly bound to specific Presto clusters which led to inevitable disruptions during maintenance windows. Additionally, users frequently overloaded individual clusters with diverse workloads ranging from lightweight ad-hoc analytics to complex data warehousing queries and resource-intensive ETL processes. This prompted us to implement a more sophisticated routing solution, one that was use case focused and not tightly bound to the specific underlying compute. To enable routing across multiple query engines within the same platform, we developed a query hint mechanism that allows users to specify their intended use case. Users append this hint to the JDBC string via the X-Presto-Extra-Credential header , which Odinâs logical routing layer then evaluates alongside multiple factors including user identity, query origin, and fallback planning. The system also assesses whether the target resource has sufficient capacity, if not, it reroutes the query to an alternative resource with available capacity. While users provide initial context through their hints, Odin makes the final routing decisions intelligently on the server side. This approach balances user input with centralized orchestration, ensuring consistent performance and resource availability. For example, say a user might specify the following connection string when connecting to the Odin platform from a Tableau client: jdbc:presto://odin.twilio.com:443/hive?SSL=true&extraCredentials=routing:athena The connection string uses the extraCredentials header to signal execution on Athena, where Odin validates query submission details, including the submitting user and tool, before determining the appropriate Athena workgroup for initial routing. Since this Tableau data source and user qualify as âcritical queries,â the system routes them to a workgroup backed by capacity reservations. However, if that workgroup has too many pending queries in the execution queue, Odinâs routing logic automatically redirects to alternative workgroups with greater available resources. When necessary, queries may ultimately route to workgroups running on on-demand capacity. Through this fallback logic, Odin provides built-in load balancing at the routing layer, ensuring optimal utilization across the underlying compute infrastructure. Here is an example workflow of how our queries are routed to Athena workgroups: Once a query has been submitted to a workgroup for execution, Odin will also log the routing decision in our tracking system based on Amazon ElastiCache for Redis so that Odinâs routing logic can maintain real-time awareness of queue depths across all Athena workgroups. Additionally, Odin uses Amazon EventBridge to integrate with Amazon Athena to keep track of a query state change and create event-based workflows. Our Redis-based query tracking system effectively handles edge cases, such as when a JDBC client terminates mid-query. Even during such unexpected interruptions, the platform consistently maintains and updates the accurate state of the query. Query history Following successful query routing to either an Athena workgroup or one of our open-source Presto clusters, Odin persists the query identifier and destination endpoint in a query history table in DynamoDB. This design utilizes a RESTful architecture where initial query submissions operate as POST requests, while subsequent status checks function as GET requests that utilize DynamoDB as the authoritative lookup mechanism to locate and poll the appropriate execution engine. By centralizing query execution records in DynamoDB rather than maintaining state on individual servers, weâve created a truly stateless system where incoming requests can be handled by any Amazon EC2 instance hosting our Odin web service. Lessons learned The transition from open-source Presto to Athena required some adaptation time, due to subtle differences in how these query engines operate. Since our Odin framework was built on the Presto driver, we needed to modify our processing approach to ensure compatibility between both systems. As we began to adopt Athena for more use cases, we noticed a difference in the record counts between Athena and the original Presto queries. We discovered this was due to open-source Presto returning results with every page containing a header column, whereas Athena results only contain the header column on the first page and subsequent pages containing records only. This difference meant that for a 60-page result set, Athena would return 59 fewer rows than open-source Presto. Once we identified this pagination behavior, we optimized Odinâs result handling logic to properly interpret and process Athenaâs format, so that queries would return accurate results. Due to the nature of using the Odin platform, most of our interactions with the Athena service are API driven so we make use of the ResultSet object with the GetQueryResults API to retrieve query execution data. Using this mechanism, the API returns the data as all VARCHAR data type, even for complex types such as row, map, or array. This created a challenge because Odin uses the Presto driver for query parsing, resulting in a type mismatch between the expected formats and actual returned data. To address this, we implemented a translation layer within the Odin framework that converts all data types to VARCHAR and handles any downstream implications of this conversion internally. These technical adjustments, while initially challenging, highlighted the importance of carefully managing the subtle differences between different query execution engines when building a unified data platform. Scale of Odin and looking ahead The Odin platform serves over 1,500 users who execute approximately 80,000 queries daily, totaling 2.5 million queries per month. Odin also powers more than 5,000 Business Intelligence (BI) reports and dashboards for Tableau and Looker. The queries are executed across our multi-engine landscape of more than 30 workgroups in Athena based on both provisioned capacity and on-demand workgroups and 4 Presto clusters on running on EC2 instances with Auto Scaling enabled that run on average 180 instances each. As Twilio continues to experience rapid growth, our Odin platform has enabled us to mature our technology stacks by both upgrading existing compute resources and integrating new technologies. We can do all this without disrupting the experience for our end users. While Odin serves as our foundation, weâre excited to continue to expand this pluggable infrastructure. Our roadmap includes migrating our self-managed open-source Presto implementation to EMR Trino, introducing Apache Spark as a compute engine via Amazon EMR Serverless or AWS Glue jobs, and integrating generative AI capabilities to intelligently route queries across Odinâs various compute options. Conclusion In this post, weâve shared how we built Odin, our unified multi-engine query platform. By combining AWS services like Amazon Athena, Amazon ElastiCache for Redis, and Amazon DynamoDB with our open-source technology stack, we created a transparent abstraction layer for users. This integration has resulted in a highly available and resilient platform environment that serves our query processing needs. By embracing this multi-engine approach, not only did we solve our query infrastructure challenges but we also established a flexible foundation that will continue to evolve with our data needs, ensuring we can deliver powerful insights at scale regardless of how technology trends shift in the future. To learn more and get started using Amazon Athena, please see the Athena User Guide . About the authors Aakash Pradeep Aakash is a Senior Software Engineer at Adobe with over 15 years of experience across ingestion, compute, storage, and query platforms. Previously, at Twilio, he worked extensively on developing the Odin platform to serve his customers query infrastructure needs. Aakash is a PrestoCon speaker, holds multiple patents in real-time analytics, and is passionate about building high-performance distributed systems. Venkatram Bondugula Venkatram is a seasoned backend engineer with over a decade of experience specializing in the design and development of scalable data platforms for big data and distributed systems. With a strong background in backend architecture and data engineering, he has built and optimized high-performance systems that power data-driven decision-making at scale. Amber Runnels Amber is a Senior Analytics Specialist Solutions Architect at AWS specializing in big data and distributed systems. She helps customers optimize workloads in the AWS data ecosystem to achieve a scalable, performant, and cost-effective architecture. Aside from technology, she is passionate about exploring the many places and cultures this world has to offer, reading novels, and building terrariums. 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24px);border-radius:40px!important;align-items:center;justify-content:center;display:inline-flex;height:var(--button-size, 44px);text-decoration:none!important;text-wrap:nowrap;cursor:pointer;position:relative;transition:all .3s ease}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_094d67e1,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_094d67e1{--button-size: 32px;--button-pad-h: 14px;--button-pad-borderless-h: 16px}[data-eb-6a8f3296] a.rgft_f7822e54>span,[data-eb-6a8f3296] button.rgft_f7822e54>span{color:var(--btn-text-color, inherit)!important}[data-eb-6a8f3296] a.rgft_f7822e54:focus-visible,[data-eb-6a8f3296] button.rgft_f7822e54:focus-visible{outline:2px solid var(--rg-color-focus-ring, #006CE0)!important;outline-offset:4px!important;transition:outline 0s}[data-eb-6a8f3296] a.rgft_f7822e54:focus:not(:focus-visible),[data-eb-6a8f3296] button.rgft_f7822e54:focus:not(:focus-visible){outline:none!important}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_303c672b,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_303c672b{--btn-text-color: var(--rg-color-text-utility-inverted, #FFFFFF);background-color:var(--rg-color-btn-primary-bg, #161D26);border:none;padding:10px var(--button-pad-borderless-h, 24px)}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_18409398,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_18409398{--btn-text-color: var(--rg-color-text-utility, #161D26);background-color:var(--rg-color-btn-secondary-bg, #FFFFFF);border-color:var(--rg-color-background-page-inverted, #0F141A)}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_090951dc,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_090951dc{--btn-text-color: var(--rg-color-text-utility, #161D26);background-color:var(--rg-color-background-object, #F3F3F7);border:none;padding:10px var(--button-pad-borderless-h, 24px)}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_15529d9f,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_15529d9f{--btn-text-color: var(--rg-color-text-utility, #161D26);background-color:var(--rg-color-btn-secondary-bg, 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button[data-rg-mode=dark].rgft_f7822e54.rgft_bb950a4e{background-image:var(--rg-gradient-a, linear-gradient(120deg, #78008a 24.25%, #b2008f 69.56%))}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_bb419678,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_bb419678{--btn-text-color: var(--rg-color-text-utility-inverted, #FFFFFF);background-color:var(--rg-color-btn-visited-bg, #656871);border-color:var(--rg-color-btn-visited-bg, #656871)}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_bb419678.rgft_18409398,[data-eb-6a8f3296] a.rgft_f7822e54.rgft_bb419678.rgft_15529d9f,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_bb419678.rgft_18409398,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_bb419678.rgft_15529d9f{--btn-text-color: var(--rg-color-text-utility, #161D26);background-color:var(--rg-color-btn-secondary-visited-bg, #FFFFFF)}[data-eb-6a8f3296] a.rgft_f7822e54.rgft_badebaf5,[data-eb-6a8f3296] button.rgft_f7822e54.rgft_badebaf5{--btn-text-color: var(--rg-color-btn-disabled-text, 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.rgft_d835af5c.rgft_852a8b78{font-family:NotoSansThai,Helvetica,Arial,sans-serif}[data-eb-6a8f3296] [data-rg-lang=zh] .rgft_d835af5c.rgft_852a8b78{font-family:NotoSansTC,Helvetica,Arial,Microsoft Yahei,\5fae\8f6f\96c5\9ed1,STXihei,\534e\6587\7ec6\9ed1,sans-ser | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/analytics/amazon-sagemaker-data-ai-governance/ | Amazon SageMaker Data & AI Governance | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon SageMaker Data & AI Governance Navigating architectural choices for a lakehouse using Amazon SageMaker by Lakshmi Nair and Saman Irfan on 12 JAN 2026 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics Permalink Comments Share Over time, several distinct lakehouse approaches have emerged. In this post, we show you how to evaluate and choose the right lakehouse pattern for your needs. A lakehouse architecture isnât about choosing between a data lake and a data warehouse. Instead, itâs an approach to interoperability where both frameworks coexist and serve different purposes within a unified data architecture. By understanding fundamental storage patterns, implementing effective catalog strategies, and using native storage capabilities, you can build scalable, high-performance data architectures that support both your current analytics needs and future innovation. AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale by Larry Weber on 07 JAN 2026 in Amazon EMR , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon OpenSearch Service , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , AWS Glue , AWS Lake Formation , AWS re:Invent , Intermediate (200) Permalink Comments Share re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, [âŠ] Reference guide for building a self-service analytics solution with Amazon SageMaker by Navnit Shukla , Ayan Majumder , and Karan Edikala on 16 DEC 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Technical How-to Permalink Comments Share In this post, we show how to use Amazon SageMaker Catalog to publish data from multiple sources, including Amazon S3, Amazon Redshift, and Snowflake. This approach enables self-service access while ensuring robust data governance and metadata management. How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker by Avinash Erupaka , Radhika Kashyap , and Modood Alvi on 12 DEC 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows. Enforce business glossary classification rules in Amazon SageMaker Catalog by Ramesh H Singh , Manny Pelaez , Pradeep Misra , and Pradyut Singh on 20 NOV 2025 in Amazon SageMaker Data & AI Governance , Announcements , Intermediate (200) Permalink Comments Share Amazon SageMaker Catalog now supports metadata enforcement rules for glossary terms classification (tagging) at the asset level. With this capability, administrators can require that assets include specific business terms or classifications. Data producers must apply required glossary terms or classifications before an asset can be published. In this post, we show how to enforce business glossary classification rules in SageMaker Catalog. Enhanced data discovery in Amazon SageMaker Catalog with custom metadata forms and rich text documentation by Ramesh H Singh , Abbas Makhdum , Harish Panwar , and Pradeep Misra on 20 NOV 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Announcements Permalink Comments Share Amazon SageMaker Catalog now supports custom metadata forms and rich text descriptions at the column level, extending existing curation capabilities for business names, descriptions, and glossary term classifications. Column-level context is essential for understanding and trusting data. This release helps organizations improve data discoverability, collaboration, and governance by letting metadata stewards document columns using structured and formatted information that aligns with internal standards. In this post, we show how to enhance data discovery in SageMaker Catalog with custom metadata forms and rich text documentation at the schema level. Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Accelerate data governance with custom subscription workflows in Amazon SageMaker by Nira Jaiswal and Ajit Tandale on 24 OCT 2025 in Advanced (300) , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share Organizations need to efficiently manage data assets while maintaining governance controls in their data marketplaces. Although manual approval workflows remain important for sensitive datasets and production systems, thereâs an increasing need for automated approval processes with less sensitive datasets. In this post, we show you how to automate subscription request approvals within SageMaker, accelerating data access for data consumers. Automate email notifications for governance teams working with Amazon SageMaker Catalog by Himanshu Sahni , Jitesh Kumar , and Rajiv Upadhyay on 21 OCT 2025 in Advanced (300) , Amazon DataZone , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Technical How-to Permalink Comments Share In this post, we show you how to create custom notifications for events occurring in SageMaker Catalog using Amazon EventBridge, AWS Lambda, and Amazon SNS. You can expand this solution to automatically integrate SageMaker Catalog with in-house enterprise workflow tools like ServiceNow and Helix. Visualize data lineage using Amazon SageMaker Catalog for Amazon EMR, AWS Glue, and Amazon Redshift by Shubham Purwar , Nitin Kumar , and Prashanthi Chinthala on 13 OCT 2025 in Amazon Athena , Amazon EMR , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , AWS Glue , Expert (400) , Technical How-to Permalink Comments Share Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including [âŠ] â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? <a data-rg-n="Link" href="/what-is/?nc1=f_cc" data-rigel-analytics="{"name":"Link","properties":{"size":1}}" cla | 2026-01-13T09:29:12 |
https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/sys/conf/#dirlist | src/sys/conf/ src/sys/conf/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src / sys / conf File Rev. Age Author Last log entry Parent Directory GENERIC 1.304 3 months jsg re-enable POOL_DEBUG ok deraadt@ files 1.749 28 hours dlg remove lacp support from trunk(4) lacp is better supported by aggr(4) . users of... makegap.sh 1.14 7 years mpi Use a new LDFLAGS variable to pass "-melf_i386_obsd" on amd64. Makes the linker... newvers.sh 1.213 3 months jsg 7.8-current ok deraadt@ param.c 1.53 5 months mvs Unlock the KERN_MAXCLUSTERS case of kern_sysctl(). The `nmbclust' and `mbuf_mem... swapgeneric.c 1.6 14 months jsg remove unneeded conf.h include defines (in the Attic) [Hide] 1.5 3 months jsg remove sys/conf/defines used by links target which was removed from conf/Makefi... files.oldconf (in the Attic) [Hide] 1.18 28 years downsj Obsolete. Show only files with tag: All tags / default branch UBC_SYNC_B UBC_SYNC_A UBC_BASE UBC SMP_SYNC_B SMP_SYNC_A SMP_BASE SMP OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN kame_19991208 HEAD Module path or alias: | 2026-01-13T09:29:12 |
https://cvsweb.openbsd.org/cgi-bin/cvsweb/src/sys/#dirlist | src/sys/ src/sys/ Click on a directory to enter that directory. Click on a file to display its revision history and to get a chance to display diffs between revisions. Current directory: [local] / src / sys File Rev. Age Author Last log entry Parent Directory adosfs/ altq/ arch/ compat/ conf/ crypto/ ddb/ dev/ gnu/ isofs/ kern/ lib/ miscfs/ msdosfs/ net/ net80211/ netatalk/ netbt/ netccitt/ netinet/ netinet6/ netipx/ netiso/ netmpls/ netnatm/ netns/ nfs/ nnpfs/ ntfs/ scsi/ stand/ sys/ tmpfs/ ufs/ uvm/ vm/ xfs/ .gitignore 1.3 2 years bluhm Bring src/sys/.gitignore in sync with src/.gitignore. OK tobhe@ Makefile 1.53 4 years visa Retire OpenBSD/sgi. OK deraadt@ README.SMP (in the Attic) [Hide] 1.1 24 years niklas branches: 1.1.2; file README.SMP was initially added on branch SMP. Show only files with tag: All tags / default branch UBC_SYNC_B UBC_SYNC_A UBC_BASE UBC SMP_SYNC_B SMP_SYNC_A SMP_BASE SMP OPENBSD_7_8_BASE OPENBSD_7_8 OPENBSD_7_7_BASE OPENBSD_7_7 OPENBSD_7_6_BASE OPENBSD_7_6 OPENBSD_7_5_BASE OPENBSD_7_5 OPENBSD_7_4_BASE OPENBSD_7_4 OPENBSD_7_3_BASE OPENBSD_7_3 OPENBSD_7_2_BASE OPENBSD_7_2 OPENBSD_7_1_BASE OPENBSD_7_1 OPENBSD_7_0_BASE OPENBSD_7_0 OPENBSD_6_9_BASE OPENBSD_6_9 OPENBSD_6_8_BASE OPENBSD_6_8 OPENBSD_6_7_BASE OPENBSD_6_7 OPENBSD_6_6_BASE OPENBSD_6_6 OPENBSD_6_5_BASE OPENBSD_6_5 OPENBSD_6_4_BASE OPENBSD_6_4 OPENBSD_6_3_BASE OPENBSD_6_3 OPENBSD_6_2_BASE OPENBSD_6_2 OPENBSD_6_1_BASE OPENBSD_6_1 OPENBSD_6_0_BASE OPENBSD_6_0 OPENBSD_5_9_BASE OPENBSD_5_9 OPENBSD_5_8_BASE OPENBSD_5_8 OPENBSD_5_7_BASE OPENBSD_5_7 OPENBSD_5_6_BASE OPENBSD_5_6 OPENBSD_5_5_BASE OPENBSD_5_5 OPENBSD_5_4_BASE OPENBSD_5_4 OPENBSD_5_3_BASE OPENBSD_5_3 OPENBSD_5_2_BASE OPENBSD_5_2 OPENBSD_5_1_BASE OPENBSD_5_1 OPENBSD_5_0_BASE OPENBSD_5_0 OPENBSD_4_9_BASE OPENBSD_4_9 OPENBSD_4_8_BASE OPENBSD_4_8 OPENBSD_4_7_BASE OPENBSD_4_7 OPENBSD_4_6_BASE OPENBSD_4_6 OPENBSD_4_5_BASE OPENBSD_4_5 OPENBSD_4_4_BASE OPENBSD_4_4 OPENBSD_4_3_BASE OPENBSD_4_3 OPENBSD_4_2_BASE OPENBSD_4_2 OPENBSD_4_1_BASE OPENBSD_4_1 OPENBSD_4_0_BASE OPENBSD_4_0 OPENBSD_3_9_BASE OPENBSD_3_9 OPENBSD_3_8_BASE OPENBSD_3_8 OPENBSD_3_7_BASE OPENBSD_3_7 OPENBSD_3_6_BASE OPENBSD_3_6 OPENBSD_3_5_BASE OPENBSD_3_5 OPENBSD_3_4_BASE OPENBSD_3_4 OPENBSD_3_3_BASE OPENBSD_3_3 OPENBSD_3_2_BASE OPENBSD_3_2 OPENBSD_3_1_BASE OPENBSD_3_1 OPENBSD_3_0_BASE OPENBSD_3_0 OPENBSD_2_9_BASE OPENBSD_2_9 OPENBSD_2_8_BASE OPENBSD_2_8 OPENBSD_2_7_BASE OPENBSD_2_7 OPENBSD_2_6_BASE OPENBSD_2_6 OPENBSD_2_5_BASE OPENBSD_2_5 OPENBSD_2_4_BASE OPENBSD_2_4 OPENBSD_2_3_BASE OPENBSD_2_3 OPENBSD_2_2_BASE OPENBSD_2_2 OPENBSD_2_1_BASE OPENBSD_2_1 OPENBSD_2_0_BASE OPENBSD_2_0 netbsd_1_1 MAIN kame_19991208 HEAD Module path or alias: | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/automate-and-orchestrate-amazon-emr-jobs-using-aws-step-functions-and-amazon-eventbridge/ | Automate and orchestrate Amazon EMR jobs using AWS Step Functions and Amazon EventBridge | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Automate and orchestrate Amazon EMR jobs using AWS Step Functions and Amazon EventBridge by Senthil Kamala Rathinam and Shashidhar Makkapati on 15 SEP 2025 in Advanced (300) , Amazon CloudWatch , Amazon EC2 , Amazon EMR , Amazon EventBridge , Analytics , AWS Step Functions , Technical How-to Permalink Comments Share Many enterprises are adopting Apache Spark for scalable data processing tasks such as extract, transform, and load (ETL), batch analytics, and data enrichment. As data pipelines evolve, the need for flexible and cost-efficient execution environments that support automation, governance, and performance at scale also evolve in parallel. Amazon EMR provides a powerful environment to run Spark workloads, and depending on workload characteristics and compliance requirements, teams can choose between fully managed options like Amazon EMR Serverless or more customizable configurations using Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) . In use cases where infrastructure control, data locality, or strict security postures are essential, such as in financial services, healthcare, or government, running transient EMR on EC2 clusters becomes a preferred choice. However, orchestrating the full lifecycle of these clusters, from provisioning to job submission and eventual teardown, can introduce operational overhead and risk if done manually. To streamline this process, the AWS Cloud offers built-in orchestration capabilities using AWS Step Functions and Amazon EventBridge . Together, these services help you automate and schedule the entire EMR job lifecycle, reducing manual intervention while optimizing cost and compliance. Step Functions provides the workflow logic to manage cluster creation, Spark job execution, and cluster termination, and EventBridge schedules these workflows based on business or operational needs. In this post, we discuss how to build a fully automated, scheduled Spark processing pipeline using Amazon EMR on EC2, orchestrated with Step Functions and triggered by EventBridge. We walk through how to deploy this solution using AWS CloudFormation , processes COVID-19 public dataset data in Amazon Simple Storage Service (Amazon S3) , and store the aggregated results in Amazon S3. This architecture is ideal for periodic or scheduled batch processing scenarios where infrastructure control, auditability, and cost-efficiency are critical. Solution overview This solution uses the publicly available COVID-19 dataset to illustrate how to build a modular, scheduled architecture for scalable and cost-efficient batch processing for time-bound data workloads.The solution follows these steps: Raw COVID-19 data in CSV format is stored in an S3 input bucket. A scheduled rule in EventBridge triggers a Step Functions workflow. The Step Functions workflow provisions a transient Amazon EMR cluster using EC2 instances. A PySpark job is submitted to the cluster to calculate COVID-19 hospital utilization data to compute monthly state-level averages of inpatient and ICU bed utilization, and COVID-19 patient percentages. The processed results are written back to an S3 output bucket. After successful job completion, the EMR cluster is automatically deleted. Logs are persisted to Amazon S3 for observability and troubleshooting. By automating this workflow, you alleviate the need to manually manage EMR clusters while gaining cost-efficiency by running compute only when needed. This architecture is ideal for periodic Spark jobs such as ETL pipelines, regulatory reporting, and batch analytics, especially when control, compliance, and customization are required.The following diagram illustrates the architecture for this use case. The infrastructure is deployed using AWS CloudFormation to provide consistency and repeatability. AWS Identity and Access Management (IAM) roles grant leastâprivilege access to Step Functions, Amazon EMR, EC2 instances, and S3 buckets, and optional AWS Key Management Service (AWS KMS) encryption can secure data at rest in Amazon S3 and Amazon CloudWatch Logs . By combining a scheduled trigger, stateful orchestration, and centralized logging, this solution delivers a fully automated, costâoptimized, and secure way to run transient Spark workloads in production. Prerequisites Before you get started, make sure you have the following prerequisites: An AWS account. If you donât have one, you can sign up for one. An IAM user with administrator access. For instructions, see Create a user with administrative access . The AWS Command Line Interface (AWS CLI) is installed on your local machine A default virtual private cloud (VPC) and subnet in the target AWS Region where you plan to run the CloudFormation template. Set up resources with AWS CloudFormation To provision the required resources using a single CloudFormation template, complete the following steps: Sign in to the AWS Management Console as an admin user. Clone the sample repository to your local machine or AWS CloudShell and navigate into the project directory. git clone https://github.com/aws-samples/sample-emr-transient-cluster-step-functions-eventbridge.git cd sample-emr-transient-cluster-step-functions-eventbridge Set an environment variable for the AWS Region where you plan to deploy the resources. Replace the placeholder with your Region code, for example, us-east-1 . export AWS_REGION=<YOUR AWS REGION> Deploy the stack using the following command. Update the stack name if needed. In this example, the stack is created with the name covid19-analysis . aws cloudformation deploy \ --template-file emr_transient_cluster_step_functions_eventbridge.yaml \ --stack-name covid19-analysis \ --capabilities CAPABILITY_IAM \ --region $AWS_REGION You can monitor the stack creation progress on the AWS CloudFormation console on the Events tab. The deployment typically completes in under 5 minutes. After the stack is successfully created, go to the Outputs tab on the AWS CloudFormation console and note the following values for use in later steps: InputBucketName OutputBucketName LogBucketName Set up the COVID-19 dataset With your infrastructure in place, complete the following steps to set up the input data: Download the COVID-19 data CSV file from HealthData.gov to your local machine. Rename the downloaded file to covid19-dataset.csv. Upload the renamed file to your S3 input bucket under the raw/ folder path. Set up the PySpark Script Complete the following steps to set up the PySpark script: Open AWS CloudShell from the console. Confirm that you are working inside the sample-emr-transient-cluster-step-functions-eventbridge directory before running the next command. Copy the PySpark script needed for this walkthrough into your input bucket: aws s3 cp covid19_processor.py s3://<InputBucketName>/scripts/ This script processes COVID-19 hospital utilization data stored as CSV files in your S3 input bucket. When running the job, provide the following command-line arguments: --input â The S3 path to the input CSV files --output â The S3 path to store the processed results The script reads the raw dataset, standardizes various date formats, and filters out records with invalid or missing dates. It then extracts key utilization metrics such as inpatient bed usage, ICU bed usage, and the percentage of beds occupied by COVID-19 patients and calculates monthly averages grouped by state. The aggregated output is saved as timestamped CSV files in the specified S3 location. This example demonstrates how you can use PySpark to efficiently clean, transform, and analyze large-scale healthcare data to gain actionable insights on hospital capacity trends during the pandemic. Configure a schedule in EventBridge The Step Functions state machine is by default scheduled to run on December 31, 2025, as a one-time execution. You can update the schedule for recurring or one-time execution as needed. Complete the following steps: On the EventBridge console, choose Schedules under Scheduler in the navigation pane. Select the schedule named <StackName>-covid19-analysis and choose Edit . Set your preferred schedule pattern. If you want to run the schedule one time, select One-time schedule for Occurrence and enter a date and time. If you want to run this on a recurring basis, select Recurring schedule . Specify the schedule type as either Cron-based schedule or Rate-based schedule as needed. Choose Next twice and choose Save schedule . Start the workflow in Step Functions Based on your EventBridge schedule, the Step Functions workflow will run automatically. For this walkthrough, complete the following steps to trigger it manually: On the Step Functions console, choose State machines in the navigation pane. Choose the state machine that begins with Covid19AnalysisStateMachine-*. Choose Start execution . In the Input section, provide the following JSON (provide the log bucket and output bucket names with the appropriate values captured earlier): { "LogUri": "s3://<LogBucketName>/logs/", "OutputS3Location": "s3://<OutputBucketName>/processed/" } Choose Start execution to initiate the workflow. Monitor the EMR job and workflow execution After you start the workflow, you can track both the Step Functions state transitions and the EMR job progress in real time on the console. Monitor the Step Functions state machine Complete the following steps to monitor the Step Functions state machine: On the Step Functions console, choose State machines in the navigation pane. Choose the state machine that begins with Covid19AnalysisStateMachine-*. Choose the running execution to view the visual workflow. Each state node will update as it progressesâgreen for success, red for failure. To explore a step, choose its node and inspect the input, output, and error details in the side pane. The following screenshot shows an example of a successfully executed workflow. Monitor the EMR cluster and EMR step Complete the following steps to monitor the EMR cluster and EMR step status: While the cluster is active, open the Amazon EMR console and choose Clusters in the navigation pane. Locate the Covid19Cluster transient EMR cluster. Initially, it will be in Starting status. On the Steps tab, you can see your Spark submit step listed. As the job progresses, the step status changes from Pending to Running to finally Completed or Failed . Choose the Applications tab to view the application UIs, in which you can access the Spark History Server and YARN Timeline Server for monitoring and troubleshooting. Monitor CloudWatch logs To enable CloudWatch logging and enhanced monitoring for your EMR on EC2 cluster, refer to Amazon EMR on EC2 â Enhanced Monitoring with CloudWatch using custom metrics and logs . This guide explains how to install and configure the CloudWatch agent using a bootstrap action, so you can stream system-level metrics (such as CPU, memory, and disk usage) and application logs from EMR nodes directly to CloudWatch. With this setup, you can gain real-time visibility into cluster health and performance, simplify troubleshooting, and retain critical logs even after the cluster is terminated. For this walkthrough, check the logs in the S3 log output location. Confirm cluster deletion When the Spark step is complete, Step Functions will automatically delete the Amazon EMR cluster. Refresh the Clusters page on the Amazon EMR console. You should see your cluster status change from Terminating to Terminated within a minute. By following these steps, you gain full end-to-end visibility into your workflow from the moment the Step Functions state machine is triggered to the automatic shutdown of the EMR cluster. You can monitor execution progress, troubleshoot issues, confirm job success, and continuously optimize your transient Spark workloads. Verify job output in Amazon S3 When the job is complete, complete the following steps to check the processed results in the S3 output bucket: On the Amazon S3 console, choose Buckets in the navigation pane. Open the output S3 bucket you noted earlier. Open the processed folder. Navigate into the timestamped subfolder to view the CSV output file. Download the CSV file to view the processed results, as shown in the following screenshot. Monitoring and troubleshooting To monitor the progress of your Spark job running on a transient EMR on EC2 cluster, use the Step Functions console. It provides real-time visibility into each state transition in your workflow, from cluster creation and job submission to cluster deletion. This makes it straightforward to track execution flow and identify where issues might occur.During job execution, you can use the Amazon EMR console to access cluster-level monitoring. This includes YARN application statuses, step-level logs, and overall cluster health. If CloudWatch logging is enabled in your job configuration, driver and executor logs stream in near real time, so you can quickly detect and diagnose errors, resource constraints, or data skew within your Spark application. After the workflow is complete, regardless of whether it succeeds or fails, you can perform a detailed post-execution analysis by reviewing the logs stored in the S3 bucket specified in the LogUri parameter. This log directory includes standard output and error logs, along with Spark history files, offering insights into execution behavior and performance metrics. For continued access to the Spark UI during job execution, you can use persistent application UIs on the EMR console. These links remain accessible even after the cluster is stopped, enabling deeper root-cause analysis and performance tuning for future runs. This visibility into both workflow orchestration and job execution can help teams optimize their Spark workloads, reduce troubleshooting time, and build confidence in their EMR automation pipelines. Clean up To avoid incurring ongoing charges, clean up the resources provisioned during this walkthrough: Empty the S3 buckets: On the Amazon S3 console, choose Buckets in the navigation pane. Select the input, output, and log buckets used in this tutorial. Choose Empty to remove all objects before deleting the buckets (optional). Delete the CloudFormation stack: On the AWS CloudFormation console, choose Stacks in the navigation pane. Select the stack you created for this solution and choose Delete . Confirm the deletion to remove associated resources. Conclusion In this post, we showed how to build a fully automated and cost-effective Spark processing pipeline using Step Functions, EventBridge, and Amazon EMR on EC2. The workflow provisions a transient EMR cluster, runs a Spark job to process data, and stops the cluster after the job completes. This approach helps reduce costs while giving you full control over the process. This solution is ideal for scheduled data processing tasks such as ETL jobs, log analytics, or batch reporting, especially when you need detailed control over infrastructure, security, and compliance settings. To get started, deploy the solution in your environment using the CloudFormation stack provided and adjust it to fit your data processing needs. Check out the Step Functions Developer Guide and Amazon EMR Management Guide to explore further. Share your feedback and ideas in the comments or connect with your AWS Solutions Architect to fine-tune this pattern for your use case. About the authors Senthil Kamala Rathinam Senthil is a Solutions Architect at Amazon Web Services, specializing in Data and Analytics for banking customers across North America. With deep expertise in Data and Analytics, AI/ML, and Generative AI, he helps organizations unlock business value through data-driven transformation. Beyond work, Senthil enjoys spending time with his family and playing badminton. Shashi Makkapati Shashi is a Senior Solutions Architect serving banking customers across North America. He specializes in data analytics, AI/ML, and generative AI, focusing on innovative solutions that transform financial organizations. Shashi is passionate about leveraging technology to solve complex business challenges in the banking sector. Outside of work, he enjoys traveling and spending quality time with his family. 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https://aws.amazon.com/blogs/big-data/how-twilio-built-a-multi-engine-query-platform-using-amazon-athena-and-open-source-presto/ | How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share Twilio is a customer engagement platform that powers real-time, personalized customer experiences for leading brands through APIs that democratize communications channels like voice, text, chat, and video. At Twilio, we manage a 20 petabyte-scale Amazon Simple Storage Service (Amazon S3) data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly, and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. A growing need for a multi-engine platform Our data platform has been built on Presto since its inception, but over the years as we expanded to support multiple business lines and diverse use cases, we began to encounter challenges related to scalability, operational overhead, and cost management. Maintaining the platform through frequent version upgrades also became difficult. These upgrades required significant time to evaluate backwards compatibility, integrate with our existing data ecosystem, and determine optimal configurations across releases. The administrative burden of upgrades and our commitment to minimizing user disruption caused our Presto version to fall behind. This prevented us from accessing the latest features and optimizations available in later releases. The adoption of Apache Hudi for our transaction-dependent critical workloads created a new requirement which our existing Presto deployment version couldnât support. We needed an up-to-date Presto or Trino compatible service to accommodate these use cases while still reducing the operational overhead of maintaining our own query infrastructure. Building a comprehensive data platform required us to balance multiple competing requirements and business constraints. We needed a solution that could support diverse workload types, from interactive analytics to ETL batch processing, while providing the flexibility to optimize compute resources based on specific use cases. We also wanted to improve upon cost management and attribution in our shared multi-tenanted query platform. Additionally, we needed to ensure that adopting any new technology did not cause any disruption to our users and maintained backward compatibility with existing systems during the transition period. Selecting Amazon Athena as our modern analytics engine Our users relied on SQL for interactive analysis, and we wanted to preserve this experience and make use of our existing jobs and application code. This meant we needed a Presto-compatible analytics service to modernize our data platform. Amazon Athena is a serverless interactive query service built on Presto and Trino that allows you to run queries using a familiar ANSI SQL interface. Athena appealed to us due to its compatibility with open-source Trino and its seamless upgrade experience. Athena helps to ease the burden of managing a large-scale query infrastructure, and with provisioned capacity , offers predictable and scalable pricing for our largest query workloads. Athenaâs workgroups provided the query and cost management capabilities we needed to efficiently support diverse teams and workload patterns with minimal overhead. The ability to blend on-demand and dedicated serverless capacity models allows us to optimize workload distribution for our requirements, achieving the flexibility and scalability needed in a managed query environment. To address latency-sensitive and predictive query workloads, we adopted provisioned capacity for its serverless capacity guarantee and workload concurrency control features. For queries that may be ad-hoc and more flexible in scheduling, we opted to use the cost-efficient multi-tenant on-demand model, which optimizes resource utilization through shared infrastructure. In parallel to migrating workloads to Athena, we also needed a way to support legacy workloads that use custom implementations of Presto features. This requirement drove us to abstract the underlying implementation, allowing us to present users with a unified interface. This would give us the flexibility key to future proof our infrastructure and use the most appropriate compute for the workload and use case. The birth of Odin The following diagram shows Twilioâs multi-engine query platform that incorporates both Amazon Athena and open-source Presto. High Level Architecture of Odinâs Query Engines Odin is a Presto-based gateway built on Zuul , an open-source L7 application gateway developed by Netflix. Zuul had already demonstrated its scalability at Twilio, having been successfully adopted by other internal teams. Since end users primarily connect to the platform via a JDBC connector using the Presto Driver (which operates through HTTP calls), Zuulâs specialization in HTTP call management made it an ideal technical choice for our needs. Odin functions as a central hub for query processing, employing a pluggable design that accommodates various query frameworks for maximum extensibility and flexibility. To interact with the Odin platform users are initially directed to an Amazon Application Load Balancer that sits in front of the Odin instances running on Amazon EC2. The Odin instances handle the authentication, routing, and entire query workflow throughout the queryâs lifetime. Amazon ElastiCache for Redis handles the query tracking for Athena and Amazon DynamoDB is responsible for the maintaining the query history. Both query engines, Amazon Athena and the Presto clusters running on Amazon EC2,are supported by the AWS Glue Data Catalog as the metastore repository and query data from our Amazon S3-based data lake. Routing queries to multiple engines We had a variety of use cases that were being served by this query platform and therefore we opted to use Amazon Athena as our primary query engine while continuing to route certain legacy workloads to our Presto clusters. Prior to our architectural redesign, we encountered operational challenges due to our end users being tightly bound to specific Presto clusters which led to inevitable disruptions during maintenance windows. Additionally, users frequently overloaded individual clusters with diverse workloads ranging from lightweight ad-hoc analytics to complex data warehousing queries and resource-intensive ETL processes. This prompted us to implement a more sophisticated routing solution, one that was use case focused and not tightly bound to the specific underlying compute. To enable routing across multiple query engines within the same platform, we developed a query hint mechanism that allows users to specify their intended use case. Users append this hint to the JDBC string via the X-Presto-Extra-Credential header , which Odinâs logical routing layer then evaluates alongside multiple factors including user identity, query origin, and fallback planning. The system also assesses whether the target resource has sufficient capacity, if not, it reroutes the query to an alternative resource with available capacity. While users provide initial context through their hints, Odin makes the final routing decisions intelligently on the server side. This approach balances user input with centralized orchestration, ensuring consistent performance and resource availability. For example, say a user might specify the following connection string when connecting to the Odin platform from a Tableau client: jdbc:presto://odin.twilio.com:443/hive?SSL=true&extraCredentials=routing:athena The connection string uses the extraCredentials header to signal execution on Athena, where Odin validates query submission details, including the submitting user and tool, before determining the appropriate Athena workgroup for initial routing. Since this Tableau data source and user qualify as âcritical queries,â the system routes them to a workgroup backed by capacity reservations. However, if that workgroup has too many pending queries in the execution queue, Odinâs routing logic automatically redirects to alternative workgroups with greater available resources. When necessary, queries may ultimately route to workgroups running on on-demand capacity. Through this fallback logic, Odin provides built-in load balancing at the routing layer, ensuring optimal utilization across the underlying compute infrastructure. Here is an example workflow of how our queries are routed to Athena workgroups: Once a query has been submitted to a workgroup for execution, Odin will also log the routing decision in our tracking system based on Amazon ElastiCache for Redis so that Odinâs routing logic can maintain real-time awareness of queue depths across all Athena workgroups. Additionally, Odin uses Amazon EventBridge to integrate with Amazon Athena to keep track of a query state change and create event-based workflows. Our Redis-based query tracking system effectively handles edge cases, such as when a JDBC client terminates mid-query. Even during such unexpected interruptions, the platform consistently maintains and updates the accurate state of the query. Query history Following successful query routing to either an Athena workgroup or one of our open-source Presto clusters, Odin persists the query identifier and destination endpoint in a query history table in DynamoDB. This design utilizes a RESTful architecture where initial query submissions operate as POST requests, while subsequent status checks function as GET requests that utilize DynamoDB as the authoritative lookup mechanism to locate and poll the appropriate execution engine. By centralizing query execution records in DynamoDB rather than maintaining state on individual servers, weâve created a truly stateless system where incoming requests can be handled by any Amazon EC2 instance hosting our Odin web service. Lessons learned The transition from open-source Presto to Athena required some adaptation time, due to subtle differences in how these query engines operate. Since our Odin framework was built on the Presto driver, we needed to modify our processing approach to ensure compatibility between both systems. As we began to adopt Athena for more use cases, we noticed a difference in the record counts between Athena and the original Presto queries. We discovered this was due to open-source Presto returning results with every page containing a header column, whereas Athena results only contain the header column on the first page and subsequent pages containing records only. This difference meant that for a 60-page result set, Athena would return 59 fewer rows than open-source Presto. Once we identified this pagination behavior, we optimized Odinâs result handling logic to properly interpret and process Athenaâs format, so that queries would return accurate results. Due to the nature of using the Odin platform, most of our interactions with the Athena service are API driven so we make use of the ResultSet object with the GetQueryResults API to retrieve query execution data. Using this mechanism, the API returns the data as all VARCHAR data type, even for complex types such as row, map, or array. This created a challenge because Odin uses the Presto driver for query parsing, resulting in a type mismatch between the expected formats and actual returned data. To address this, we implemented a translation layer within the Odin framework that converts all data types to VARCHAR and handles any downstream implications of this conversion internally. These technical adjustments, while initially challenging, highlighted the importance of carefully managing the subtle differences between different query execution engines when building a unified data platform. Scale of Odin and looking ahead The Odin platform serves over 1,500 users who execute approximately 80,000 queries daily, totaling 2.5 million queries per month. Odin also powers more than 5,000 Business Intelligence (BI) reports and dashboards for Tableau and Looker. The queries are executed across our multi-engine landscape of more than 30 workgroups in Athena based on both provisioned capacity and on-demand workgroups and 4 Presto clusters on running on EC2 instances with Auto Scaling enabled that run on average 180 instances each. As Twilio continues to experience rapid growth, our Odin platform has enabled us to mature our technology stacks by both upgrading existing compute resources and integrating new technologies. We can do all this without disrupting the experience for our end users. While Odin serves as our foundation, weâre excited to continue to expand this pluggable infrastructure. Our roadmap includes migrating our self-managed open-source Presto implementation to EMR Trino, introducing Apache Spark as a compute engine via Amazon EMR Serverless or AWS Glue jobs, and integrating generative AI capabilities to intelligently route queries across Odinâs various compute options. Conclusion In this post, weâve shared how we built Odin, our unified multi-engine query platform. By combining AWS services like Amazon Athena, Amazon ElastiCache for Redis, and Amazon DynamoDB with our open-source technology stack, we created a transparent abstraction layer for users. This integration has resulted in a highly available and resilient platform environment that serves our query processing needs. By embracing this multi-engine approach, not only did we solve our query infrastructure challenges but we also established a flexible foundation that will continue to evolve with our data needs, ensuring we can deliver powerful insights at scale regardless of how technology trends shift in the future. To learn more and get started using Amazon Athena, please see the Athena User Guide . About the authors Aakash Pradeep Aakash is a Senior Software Engineer at Adobe with over 15 years of experience across ingestion, compute, storage, and query platforms. Previously, at Twilio, he worked extensively on developing the Odin platform to serve his customers query infrastructure needs. Aakash is a PrestoCon speaker, holds multiple patents in real-time analytics, and is passionate about building high-performance distributed systems. Venkatram Bondugula Venkatram is a seasoned backend engineer with over a decade of experience specializing in the design and development of scalable data platforms for big data and distributed systems. With a strong background in backend architecture and data engineering, he has built and optimized high-performance systems that power data-driven decision-making at scale. Amber Runnels Amber is a Senior Analytics Specialist Solutions Architect at AWS specializing in big data and distributed systems. She helps customers optimize workloads in the AWS data ecosystem to achieve a scalable, performant, and cost-effective architecture. Aside from technology, she is passionate about exploring the many places and cultures this world has to offer, reading novels, and building terrariums. 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https://aws.amazon.com/blogs/big-data/amazon-emr-serverless-eliminates-local-storage-provisioning-reducing-data-processing-costs-by-up-to-20/ | Amazon EMR Serverless eliminates local storage provisioning, reducing data processing costs by up to 20% | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Amazon EMR Serverless eliminates local storage provisioning, reducing data processing costs by up to 20% by Karthik Prabhakar , Matt Tolton , Neil Mukerje , and Ravi Kumar Singh on 06 JAN 2026 in Amazon EMR , Analytics , Announcements , Intermediate (200) , Serverless Permalink Comments Share At AWS re:Invent 2025, Amazon Web Services (AWS) announced serverless storage for Amazon EMR Serverless , a new capability that eliminates the need configure local disks for Apache Spark workloads. This reduces data processing costs by up to 20% while eliminating job failures from disk capacity constraints. With serverless storage, Amazon EMR Serverless automatically handles intermediate data operations, such as shuffle, on your behalf. You pay only for compute and memoryâno storage charges. By decoupling storage from compute, Spark can release idle workers immediately, reducing costs throughout the job lifecycle. The following image shows the serverless storage for EMR Serverless announcement from the AWS re:Invent 2025 keynote: The challenge: Sizing local disk storage Running Apache Spark workloads requires sizing local disk storage for shuffle operationsâwhere Spark redistributes data across executors during joins, aggregations, and sorts. This requires analyzing job histories to estimate disk requirements, leading to two common problems: overprovisioning wastes money on unused capacity, and under provisioning causes job failures when disk space runs out. Most customers overprovision local storage to ensure jobs complete successfully in production. Data skew compounds this further. When one executor handles a disproportionately large partition, that executor takes significantly longer to complete while other workers sit idle. If you didnât provision enough disk for that skewed executor, the job fails entirelyâmaking data skew one of the top causes of Spark job failures. However, the problem extends beyond capacity planning. Because shuffle data couples tightly to local disks, Spark executors pin to worker nodes even when compute requirements drop between job stages. This prevents Spark from releasing workers and scaling down, inflating compute costs throughout the job lifecycle. When a worker node fails, Spark must recompute the shuffle data stored on that node, causing delays and inefficient resource usage. How it works Serverless storage for Amazon EMR Serverless addresses these challenges by offloading shuffle operations from individual compute workers onto a separate, elastic storage layer. Instead of storing critical data on local disks attached to Spark executors, serverless storage automatically provisions and scales high-performance remote storage as your job runs. The architecture provides several key benefits. First, compute and storage scale independentlyâSpark can acquire and release workers as needed across job stages without worrying about preserving locally stored data. Second, shuffle data is evenly distributed across the serverless storage layer, eliminating data skew bottlenecks that occur when some executors handle disproportionately large shuffle partitions. Third, if a worker node fails, your job continues processing without delays or reruns because data is reliably stored outside individual compute workers. Serverless storage is provided at no additional charge, and it eliminates the cost associated with local storage. Instead of paying for fixed disk capacity sized for maximum potential I/O loadâcapacity that often sits idle during lighter workloadsâyou can use serverless storage without incurring storage costs. You can focus your budget on compute resources that directly process your data, not on managing and overprovisioning disk storage. Technical innovation brings three breakthroughs Serverless storage introduces three fundamental innovations that solve Sparkâs shuffle bottlenecks: multi-tier aggregation architecture, purpose-built networking, and true storage-compute decoupling. Apache Sparkâs shuffle mechanism has a core constraint: each mapper independently writes output as small files, and each reducer must fetch data from potentially thousands of workers. In a large-scale job with 10,000 mappers and 1,000 reducers, this creates 10 million individual data exchanges. Serverless storage aggregates early and intelligentlyâmappers stream data to an aggregation layer that consolidates shuffle data in memory before committing to storage. Whereas individual shuffle write and fetch operations might show slightly higher latency due to network round-trips compared to local disk I/O, the overall job performance improves by transforming millions of tiny I/O operations into a smaller number of large, sequential operations. Traditional Spark shuffle creates a mesh network where each worker maintains connections to potentially hundreds of other workers, spending significant CPU on connection management rather than data processing. We built a custom networking stack where each mapper opens a single persistent remote procedure call (RPC) connection to our aggregator layer, eliminating the mesh complexity. Although individual shuffle operations might show slightly higher latency due to network round trips compared to local disk I/O, overall job performance improves through better resource utilization and elastic scaling. Workers no longer run a shuffle serviceâthey focus entirely on processing your data. Traditional Amazon EMR Serverless jobs store shuffle data on local disks, coupling data lifecycle to worker lifecycleâidle workers canât terminate without losing shuffle data. Serverless storage decouples these entirely by storing shuffle data in AWS managed storage with opaque handles tracked by the driver. Workers can terminate immediately after completing tasks without data loss, enabling elastic scaling. In funnel-shaped queries where early stages require massive parallelism that narrows as data aggregates, weâre seeing up to 80% compute cost reduction in benchmarks by releasing idle workers instantly. The following diagram illustrates instant worker release in funnel-shaped queries. Our aggregator layer integrates directly with AWS Identity and Access Management (IAM), AWS Lake Formation , and fine-grained access control systems, providing job-level data isolation with access controls that match source data permissions. Getting started Serverless storage is available in multiple AWS Regions . For the current list of supported Regions, refer to the Amazon EMR User Guide . New applications Serverless storage can be enabled for new applications starting with Amazon EMR release 7.12. Follow these steps: Create an Amazon EMR Serverless application with Amazon EMR 7.12 or later: aws emr-serverless create-application \ --type "SPARK" \ --name my-application \ --release-label emr-7.12.0 \ --runtime-configuration '[{ "classification": "spark-defaults", "properties": { "spark.aws.serverlessStorage.enabled": "true" } }]' \ --region us-east-1 Submit your Spark job: aws emr-serverless start-job-run \ --application-id <application-id> \ --execution-role-arn <execution-role-arn> \ --job-driver '{ "sparkSubmit": { "entryPoint": "s3://<bucket>/<your_script.py>", "sparkSubmitParameters": "--conf spark.executor.cores=4 --conf spark.executor.memory=20g --conf spark.driver.cores=4 --conf spark.driver.memory=8g --conf spark.executor.instances=10" } }' Existing applications You can enable serverless storage for existing applications on Amazon EMR 7.12 or later by updating your application settings. To enable serverless storage using AWS Command Line Interface (AWS CLI), enter the following command: aws emr-serverless update-application \ --application-id <application-id> \ --runtime-configuration '[{ "classification": "spark-defaults", "properties": { "spark.aws.serverlessStorage.enabled": "true" } }]' To enable serverless storage using Amazon EMR Studio UI, navigate to your application in Amazon EMR Studio, go to Configuration , and add the Spark property spark.aws.serverlessStorage.enabled=true in the spark-defaults classification. Job-level configuration You can also enable serverless storage for specific jobs, even when itâs not enabled at the application level: aws emr-serverless start-job-run \ --application-id <application-id> \ --execution-role-arn <execution-role-arn> \ --job-driver '{ "sparkSubmit": { "entryPoint": "s3://<bucket>/<your_script.py>", "sparkSubmitParameters": "--conf spark.executor.cores=4 --conf spark.executor.memory=20g --conf spark.aws.serverlessStorage.enabled=true" } }' (Optional) Disabling serverless storage If you prefer to continue using local disks, you can disable serverless storage by omitting the spark.aws.serverlessStorage.enabled configuration or setting it to false at either the application or job level: spark.aws.serverlessStorage.enabled=false To use traditional local disk provisioning, configure the appropriate disk type and size for your application workers. Monitoring and cost tracking You can monitor elastic shuffle usage through standard Spark UI metrics and track costs at the application level in AWS Cost Explorer and AWS Cost and Usage Reports . The service automatically handles performance optimization and scaling, so you donât need to tune configuration parameters. When to use serverless storage Serverless storage delivers the most value for workloads with substantial shuffle operationsâtypically jobs that shuffle more than 10 GB of data (and less than 200 G per job, the limitation as of this writing). These include: Large-scale data processing with heavy aggregations and joins Sort-heavy analytics workloads Iterative algorithms that repeatedly access the same datasets Jobs with unpredictable shuffle sizes benefit particularly well because serverless storage automatically scales capacity up and down based on real-time demand. For workloads with minimal shuffle activity or very short duration (under 2â3 minutes), the benefits might be limited. In these cases, the overhead of remote storage access might outweigh the advantages of elastic scaling. Security and data lifecycle Your data is stored in serverless storage only while your job is running and is automatically deleted when your job is completed. Because Amazon EMR Serverless batch jobs can run for up to 24 hours, your data will be stored for no longer than this maximum duration. Serverless storage encrypts your data both in transit between your Amazon EMR Serverless application and the serverless storage layer and at rest while temporarily stored, using AWS managed encryption keys. The service uses an IAM based security model with job-level data isolation, which means that one job canât access the shuffle data of another job. Serverless storage maintains the same security standards as Amazon EMR Serverless, with enterprise-grade security controls throughout the processing lifecycle. Conclusion Serverless storage represents a fundamental shift in how we approach data processing infrastructure, eliminating manual configuration, aligning costs to actual usage, and improving reliability for I/O intensive workloads. By offloading shuffle operations to a managed service, data engineers can focus on building analytics rather than managing storage infrastructure. To learn more about serverless storage and get started, visit the Amazon EMR Serverless documentation . About the authors Karthik Prabhakar Karthik is a Data Processing Engines Architect for Amazon EMR at AWS. He specializes in distributed systems architecture and query optimization, working with customers to solve complex performance challenges in large-scale data processing workloads. His focus spans engine internals, cost optimization strategies, and architectural patterns that enable customers to run petabyte-scale analytics efficiently. Ravi Kumar Ravi is a Senior Product Manager Technical at Amazon Web Services, specializing in exabyte-scale data infrastructure and analytics platforms. He helps customers unlock insights from structured and unstructured data using open-source technologies and cloud computing. Outside of work, Ravi enjoys exploring emerging trends in data science and machine learning. Matt Tolton Matt is a Senior Principal Engineer at Amazon Web Services. Neil Mukerje Neil is a Principal Product Manager at Amazon Web Services. Loading comments⊠Resources Amazon Athena Amazon EMR Amazon Kinesis Amazon MSK Amazon QuickSight Amazon Redshift AWS Glue Follow Twitter Facebook LinkedIn Twitch Email Updates Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? 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https://aws.amazon.com/blogs/big-data/category/post-types/customer-solutions/ | Customer Solutions | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Customer Solutions How Taxbit achieved cost savings and faster processing times using Amazon S3 Tables by Larry Christensen , Derek Ziehl , Pranjal Gururani , and Washim Nawaz on 18 DEC 2025 in Amazon S3 Tables , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share In this post, we discuss how Taxbit partnered with Amazon Web Services (AWS) to streamline their crypto tax analytics solution using Amazon S3 Tables, achieving 82% cost savings and five times faster processing times. How Socure achieved 50% cost reduction by migrating from self-managed Spark to Amazon EMR Serverless by Junaid Effendi, Pengyu Wang and Raj Ramasubbu on 15 DEC 2025 in Advanced (300) , Amazon EMR , Customer Solutions , Serverless Permalink Comments Share Socure is one of the leading providers of digital identity verification and fraud solutions. Socureâs data science environment includes a streaming pipeline called Transaction ETL (TETL), built on OSS Apache Spark running on Amazon EKS. TETL ingests and processes data volumes ranging from small to large datasets while maintaining high-throughput performance. In this post, we show how Socure was able to achieve 50% cost reduction by migrating the TETL streaming pipeline from self-managed spark to Amazon EMR serverless. How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker by Avinash Erupaka , Radhika Kashyap , and Modood Alvi on 12 DEC 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows. Medidataâs journey to a modern lakehouse architecture on AWS by Mike Araujo , Ashley Chen , Ian Beatty , and Sandeep Adwankar on 26 NOV 2025 in Amazon Kinesis , Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Amazon Simple Storage Service (S3) , Analytics , AWS Glue , Case Study , Customer Solutions Permalink Comments Share In this post, we show you how Medidata created a unified, scalable, real-time data platform that serves thousands of clinical trials worldwide with AWS services, Apache Iceberg, and a modern lakehouse architecture. How Octus achieved 85% infrastructure cost reduction with zero downtime migration to Amazon OpenSearch Service by Vaibhav Sabharwal , Andre Kurait , Harmandeep Sethi, Serhii Shevchenko, Govind Bajaj, Virendra Shinde , and Brian Presley on 26 NOV 2025 in Amazon OpenSearch Service , Customer Solutions , Intermediate (200) Permalink Comments Share This post highlights how Octus migrated its Elasticsearch workloads running on Elastic Cloud to Amazon OpenSearch Service. The journey traces Octusâs shift from managing multiple systems to adopting a cost-efficient solution powered by OpenSearch Service. How Yelp modernized its data infrastructure with a streaming lakehouse on AWS by Umesh Dangat , Ali Alemi , Bryan Spaulding , and Toby Cole on 13 NOV 2025 in Amazon Managed Streaming for Apache Kafka (Amazon MSK) , Architecture , Customer Solutions , Intermediate (200) Permalink Comments Share This is a guest post by Umesh Dangat, Senior Principal Engineer for Distributed Services and Systems at Yelp, and Toby Cole, Principle Engineer for Data Processing at Yelp, in partnership with AWS. Yelp processes massive amounts of user data dailyâover 300 million business reviews, 100,000 photo uploads, and countless check-ins. Maintaining sub-minute data freshness with [âŠ] Scaling data governance with Amazon DataZone: Covestro success story by Jörg Janssen , Mousam Majhi , Giuseppe Perillo , and Maddyzeth Ariza on 03 NOV 2025 in Amazon DataZone , Analytics , AWS Glue , Customer Solutions , Intermediate (200) Permalink Comments Share In this post, we show you how Covestro transformed its data architecture by implementing Amazon DataZone and AWS Serverless Data Lake Framework, transitioning from a centralized data lake to a data mesh architecture. The implementation enabled streamlined data access, better data quality, and stronger governance at scale, achieving a 70% reduction in time-to-market for over 1,000 data pipelines. Stifelâs approach to scalable Data Pipeline Orchestration in Data Mesh by Srinivas Kandi, Hossein Johari, Ahmad Rawashdeh, Lei Meng and Amit Maindola on 21 OCT 2025 in Advanced (300) , Analytics , Customer Solutions Permalink Comments Share Stifel Financial Corp, a diversified financial services holding company is expanding its data landscape that requires an orchestration solution capable of managing increasingly complex data pipeline operations across multiple business domains. Traditional time-based scheduling systems fall short in addressing the dynamic interdependencies between data products, requires event-driven orchestration. Key challenges include coordinating cross-domain dependencies, maintaining data consistency across business units, meeting stringent SLAs, and scaling effectively as data volumes grow. Without a flexible orchestration solution, these issues can lead to delayed business operations and insights, increased operational overhead, and heightened compliance risks due to manual interventions and rigid scheduling mechanisms that cannot adapt to evolving business needs. In this post, we walk through how Stifel Financial Corp, in collaboration with AWS ProServe, has addressed these challenges by building a modular, event-driven orchestration solution using AWS native services that enables precise triggering of data pipelines based on dependency satisfaction, supporting near real-time responsiveness and cross-domain coordination. How Twilio built a multi-engine query platform using Amazon Athena and open-source Presto by Amber Runnels , Aakash Pradeep , and Venkatram Bondugula on 21 OCT 2025 in Amazon Athena , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share At Twilio, we manage a 20 petabyte-scale Amazon S3 data lake that serves the analytics needs of over 1,500 users, processing 2.5 million queries monthly and scanning an average of 85 PB of data. To meet our growing demands for scalability, emerging technology support, and data mesh architecture adoption, we built Odin, a multi-engine query platform that provides an abstraction layer built on top of Presto Gateway. In this post, we discuss how we designed and built Odin, combining Amazon Athena with open-source Presto to create a flexible, scalable data querying solution. Breaking down data silos: Volkswagenâs approach with Amazon DataZone by Bandana Das , Anirban Saha , Sindi Cali , and Stoyan Stoyanov on 07 OCT 2025 in Advanced (300) , Amazon DataZone , Automotive , Customer Solutions , Manufacturing , Technical How-to Permalink Comments Share In this post, we introduce Amazon DataZone and explore how Volkswagen used Amazon DataZone to build their data mesh, tackle the challenges encountered, and break the data silos. â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases Resources Getting Started Training <a data-rg-n="Link" href="/trust-center/?nc1=f_cc" data-rigel-analytics=" | 2026-01-13T09:29:12 |
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https://aws.amazon.com/blogs/big-data/category/learning-levels/intermediate-200/ | Intermediate (200) | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Intermediate (200) AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale by Larry Weber on 07 JAN 2026 in Amazon EMR , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon OpenSearch Service , Amazon Redshift , Amazon SageMaker Data & AI Governance , Amazon SageMaker Unified Studio , Analytics , AWS Glue , AWS Lake Formation , AWS re:Invent , Intermediate (200) Permalink Comments Share re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, [âŠ] Amazon EMR Serverless eliminates local storage provisioning, reducing data processing costs by up to 20% by Karthik Prabhakar , Matt Tolton , Neil Mukerje , and Ravi Kumar Singh on 06 JAN 2026 in Amazon EMR , Analytics , Announcements , Intermediate (200) , Serverless Permalink Comments Share In this post, youâll learn how Amazon EMR Serverless eliminates the need to configure local disk storage for Apache Spark workloads through a new serverless storage capability. We explain how this feature automatically handles shuffle operations, reduces data processing costs by up to 20%, prevents job failures from disk capacity constraints, and enables elastic scaling by decoupling storage from compute. How Taxbit achieved cost savings and faster processing times using Amazon S3 Tables by Larry Christensen , Derek Ziehl , Pranjal Gururani , and Washim Nawaz on 18 DEC 2025 in Amazon S3 Tables , Analytics , Customer Solutions , Intermediate (200) Permalink Comments Share In this post, we discuss how Taxbit partnered with Amazon Web Services (AWS) to streamline their crypto tax analytics solution using Amazon S3 Tables, achieving 82% cost savings and five times faster processing times. Power data ingestion into Splunk using Amazon Data Firehose by Tarik Makota , Mitali Sheth , Roy Arsan , and Yashika Jain on 17 DEC 2025 in Amazon Data Firehose , Amazon Kinesis , Intermediate (200) , Technical How-to Permalink Comments Share With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose. Accelerate Apache Hive read and write on Amazon EMR using enhanced S3A by Ramesh Kandasamy , Giovanni Matteo Fumarola , Himanshu Mishra , Paramvir Singh , and Anmol Sundaram on 15 DEC 2025 in Amazon EMR , Analytics , Announcements , Intermediate (200) Permalink Comments Share In this post, we demonstrate how Apache Hive on Amazon EMR 7.10 delivers significant performance improvements for both read and write operations on Amazon S3. Introducing AWS Glue 5.1 for Apache Spark by Chiho Sugimoto , Bo Li , Kartik Panjabi , Peter Manastyrny , Noritaka Sekiyama , and Peter Tsai on 09 DEC 2025 in Announcements , AWS Glue , Intermediate (200) Permalink Comments Share AWS recently announced Glue 5.1, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue 5.1 upgrades the Spark engines to Apache Spark 3.5.6, giving you newer Spark release along with the newer dependent libraries so you can develop, run, and scale your data integration workloads and get insights faster. In this post, we describe whatâs new in AWS Glue 5.1, key highlights on Spark and related libraries, and how to get started on AWS Glue 5.1. Achieve 2x faster data lake query performance with Apache Iceberg on Amazon Redshift by Kalaiselvi Kamaraj , Aamer Shah , Fabian Nagel , Ravi Animi , and Stefan Gromoll on 26 NOV 2025 in Amazon Redshift , Announcements , Intermediate (200) Permalink Comments Share In 2025, Amazon Redshift delivered several performance optimizations that improved query performance over twofold for Iceberg workloads on Amazon Redshift Serverless, delivering exceptional performance and cost-effectiveness for your data lake workloads. In this post, we describe some of the optimizations that led to these performance gains. Accelerate data lake operations with Apache Iceberg V3 deletion vectors and row lineage by Ron Ortloff on 26 NOV 2025 in Amazon EMR , Amazon SageMaker , Amazon Simple Storage Service (S3) , Announcements , AWS Glue , Intermediate (200) , Technical How-to Permalink Comments Share In this post, we walk you through the new capabilities in Iceberg V3, explain how deletion vectors and row lineage address these challenges, explore real-world use cases across industries, and provide practical guidance on implementing Iceberg V3 features across AWS analytics, catalog, and storage services. How Octus achieved 85% infrastructure cost reduction with zero downtime migration to Amazon OpenSearch Service by Vaibhav Sabharwal , Andre Kurait , Harmandeep Sethi, Serhii Shevchenko, Govind Bajaj, Virendra Shinde , and Brian Presley on 26 NOV 2025 in Amazon OpenSearch Service , Customer Solutions , Intermediate (200) Permalink Comments Share This post highlights how Octus migrated its Elasticsearch workloads running on Elastic Cloud to Amazon OpenSearch Service. The journey traces Octusâs shift from managing multiple systems to adopting a cost-efficient solution powered by OpenSearch Service. Introducing Cluster Insights: Unified monitoring dashboard for Amazon OpenSearch Service clusters by Siddhant Gupta , Gagan Juneja , Jinhwan Hyon , and Varunsrivathsa Venkatesha on 21 NOV 2025 in Amazon OpenSearch Service , Announcements , Intermediate (200) Permalink Comments Share This blog will guide you through setting up and using Cluster Insights, including key features and metrics. By the conclusion, youâll understand how to use Cluster insights to recognize and address performance and resiliency issues within your OpenSearch Service clusters. â Older posts Create an AWS account Learn What Is AWS? What Is Cloud Computing? What Is Agentic AI? Cloud Computing Concepts Hub AWS Cloud Security What's New Blogs Press Releases Resources Getting Started Training AWS Trust Center AWS Solutions Library <a data-rg-n="Link" href="/architecture/?nc1=f_cc" data-rigel-analytics="{"name":"Link" | 2026-01-13T09:29:12 |
https://aws.amazon.com/blogs/big-data/category/analytics/amazon-sagemaker-lakehouse/ | Amazon SageMaker Lakehouse | AWS Big Data Blog Skip to Main Content Filter: All English Contact us AWS Marketplace Support My account Search Filter: All Sign in to console Create account AWS Blogs Home Blogs Editions AWS Big Data Blog Category: Amazon SageMaker Lakehouse Navigating architectural choices for a lakehouse using Amazon SageMaker by Lakshmi Nair and Saman Irfan on 12 JAN 2026 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics Permalink Comments Share Over time, several distinct lakehouse approaches have emerged. In this post, we show you how to evaluate and choose the right lakehouse pattern for your needs. A lakehouse architecture isnât about choosing between a data lake and a data warehouse. Instead, itâs an approach to interoperability where both frameworks coexist and serve different purposes within a unified data architecture. By understanding fundamental storage patterns, implementing effective catalog strategies, and using native storage capabilities, you can build scalable, high-performance data architectures that support both your current analytics needs and future innovation. How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker by Avinash Erupaka , Radhika Kashyap , and Modood Alvi on 12 DEC 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share In this post, we discuss how Bayer AG used the next generation of Amazon SageMaker to build a cloud-based Pharma R&D Data Science Ecosystem (DSE) that unified data ingestion, storage, analytics, and AI/ML workflows. Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog by Sneha Rao , Abbas Makhdum , Deepmala Agarwal , Santhosh Padmanabhan , and Viral Thakkar on 18 NOV 2025 in Amazon S3 Tables , Amazon SageMaker , Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Expert (400) , Technical How-to Permalink Comments Share In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. Youâll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation. Break down data silos and seamlessly query Iceberg tables in Amazon SageMaker from Snowflake by Nidhi Gupta and Andries Engelbrecht on 15 SEP 2025 in Advanced (300) , Amazon SageMaker Lakehouse , Amazon Simple Storage Service (S3) , AWS Glue , AWS Lake Formation , Partner solutions , S3 Select , Technical How-to Permalink Comments Share This blog post discusses how to create a seamless integration between Amazon SageMaker Lakehouse and Snowflake for modern data analytics. It specifically demonstrates how organizations can enable Snowflake to access tables in AWS Glue Data Catalog (stored in S3 buckets) through SageMaker Lakehouse Iceberg REST Catalog, with security managed by AWS Lake Formation. The post provides a detailed technical walkthrough of implementing this integration, including creating IAM roles and policies, configuring Lake Formation access controls, setting up catalog integration in Snowflake, and managing data access permissions. While four different patterns exist for accessing Iceberg tables from Snowflake, the blog focuses on the first pattern using catalog integration with SigV4 authentication and Lake Formation credential vending. The Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control for federated catalogs by Sandeep Adwankar , Aarthi Srinivasan , and Srividya Parthasarathy on 29 AUG 2025 in Amazon SageMaker , Amazon SageMaker Lakehouse , Analytics , Announcements , AWS Big Data , Intermediate (200) Permalink Comments Share We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless. Zero-ETL: How AWS is tackling data integration challenges by Nikki Rouda on 26 AUG 2025 in Amazon Redshift , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , AWS Glue Permalink Comments Share In this blog post, we show you how Amazon Web Services (AWS) is simplifying data integration with zero-ETL while realizing performance benefits and cost optimizations. As organizations gather data for analytics and AI, they are increasingly finding themselves caught in a complex web of extract, transform, and load (ETL) pipelinesâthe traditional backbone of data integration. [âŠ] Guide to adopting Amazon SageMaker Unified Studio from ATPCOâs Journey by Mitesh Patel , Nikki Rouda , Raj Samineni , and Saurabh Rawat on 18 AUG 2025 in Advanced (300) , Amazon SageMaker , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Customer Solutions Permalink Comments Share ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio. The Amazon SageMaker lakehouse architecture now automates optimization configuration of Apache Iceberg tables on Amazon S3 by Tomohiro Tanaka , Siddharth Padmanabhan Ramanarayanan , Sandeep Adwankar , and Noritaka Sekiyama on 08 AUG 2025 in Amazon SageMaker Lakehouse , Analytics , Announcements , AWS Glue , AWS Lake Formation , Technical How-to Permalink Comments Share The Amazon SageMaker lakehouse architecture now automates optimization of Iceberg tables stored in Amazon S3 with catalog-level configuration, optimizing storage in your Iceberg tables and improving query performance. This post demonstrates an end-to-end flow to enable catalog level table optimization setting. Accelerate your data quality journey for lakehouse architecture with Amazon SageMaker, Apache Iceberg on AWS, Amazon S3 tables, and AWS Glue Data Quality by Brody Pearman , Narayani Ambashta , Shiv Narayanan , and Shriya Vanvari on 28 JUL 2025 in Amazon SageMaker Lakehouse , Analytics , AWS Glue Permalink Comments Share This post explores how you can use AWS Glue Data Quality to maintain data quality of S3 Tables and Apache Iceberg tables on general purpose S3 buckets. Weâll discuss strategies for verifying the quality of published data and how these integrated technologies can be used to implement effective data quality workflows. Develop and monitor a Spark application using existing data in Amazon S3 with Amazon SageMaker Unified Studio by Amit Maindola and Abhilash Nagilla on 09 JUL 2025 in Amazon SageMaker Data & AI Governance , Amazon SageMaker Lakehouse , Amazon SageMaker Unified Studio , Analytics , Technical How-to Permalink Comments Share In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools. â Older posts <div data-rg-n="Grid" class="rgft_11eb7627 rgft_85859631" style="--rg-grid-gap:var(--rg-margin- | 2026-01-13T09:29:12 |
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