content large_stringlengths 3 20.5k | url large_stringlengths 53 192 ⌀ | branch large_stringclasses 4
values | source large_stringclasses 51
values | embeddings listlengths 384 384 | score float64 -0.21 0.65 |
|---|---|---|---|---|---|
# Vertex AI Pipeline This repository demonstrates end-to-end [MLOps process](https://services.google.com/fh/files/misc/practitioners\_guide\_to\_mlops\_whitepaper.pdf) using [Vertex AI](https://cloud.google.com/vertex-ai) platform and [Smart Analytics](https://cloud.google.com/solutions/smart-analytics) technology capa... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_pipeline/README.md | main | gcp-professional-services | [
-0.12110676616430283,
-0.012424043379724026,
-0.0070341601967811584,
0.011045748367905617,
0.006009044591337442,
-0.022786425426602364,
-0.024729551747441292,
-0.0008118136902339756,
-0.10183680057525635,
0.016008438542485237,
-0.028243377804756165,
-0.029343638569116592,
0.00688063725829124... | 0.15399 |
custom serving image is not necessary if your choosen framework is supported by [pre-built-container](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers), which are organized by machine learning (ML) framework and framework version, provide HTTP prediction servers that you can use to serve predict... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_pipeline/README.md | main | gcp-professional-services | [
-0.05464429780840874,
-0.02220374159514904,
0.034596119076013565,
0.02076021023094654,
0.06257830560207367,
-0.004484002478420734,
-0.021642060950398445,
0.03876514360308647,
-0.031592369079589844,
0.058926139026880264,
-0.007890284061431885,
-0.03747614473104477,
0.022548718377947807,
0.0... | 0.042371 |
# Instrumenting Web Applications End-to-End with Stackdriver and OpenTelemetry This tutorial demonstrates instrumenting a web application end-to-end, from the browser to the backend application, including logging, monitoring, and tracing with OpenTelemetry and Stackdriver to run for a load test. It shows how to collect... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/web-instrumentation/README.md | main | gcp-professional-services | [
-0.05815305933356285,
-0.04030333459377289,
0.041775893419981,
0.005646936129778624,
-0.03747949376702309,
-0.10704853385686874,
-0.07195582240819931,
0.023120056837797165,
-0.02953064627945423,
0.027204789221286774,
-0.06987796723842621,
-0.030132248997688293,
0.028384869918227196,
-0.089... | 0.006152 |
if you want to live on the edge gcloud beta container clusters create $NAME \ --num-nodes 1 \ --enable-autoscaling --min-nodes 1 --max-nodes 4 \ --enable-basic-auth \ --issue-client-certificate \ --release-channel $CHANNEL \ --zone $ZONE \ --enable-stackdriver-kubernetes ``` Change the project id in file `k8s/ot-servic... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/web-instrumentation/README.md | main | gcp-professional-services | [
-0.001295908004976809,
0.00568812619894743,
0.06507793813943863,
-0.026627229526638985,
-0.045545753091573715,
-0.05732082575559616,
-0.008412009105086327,
-0.007766475901007652,
0.028912128880620003,
0.08376877754926682,
0.014019086956977844,
-0.06839583069086075,
0.024971894919872284,
-0... | 0.018549 |
send a request from the command line with cURL ```shell EXTERNAL\_IP=[from kubectl get ingress command] REQUEST\_ID=1234567889 # A random number # See W3C Trace Context for format TRACE=00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01 MILLIS=`date +%s%N | cut -b1-13` curl "http://$EXTERNAL\_IP/data/$REQUEST\_ID"... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/web-instrumentation/README.md | main | gcp-professional-services | [
0.01440480723977089,
0.03355755656957626,
-0.02301076054573059,
0.01203075610101223,
-0.013399552553892136,
-0.030545417219400406,
0.032236259430646896,
-0.010510541498661041,
0.10463955998420715,
0.05747319012880325,
-0.00963857863098383,
-0.10362179577350616,
-0.025634216144680977,
-0.08... | 0.076281 |
Copyright 2024 Google. This software is provided as-is, without warranty or representation for any use or purpose. Your use of it is subject to your agreement with Google. # Five9 Voicestream Integration with Agent Assist This is a PoC to integrate Five9 Voicestream with Agent Assist. ## Project Structure ``` . ├── ass... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ccai-agentassist-five9-grpc/README.md | main | gcp-professional-services | [
-0.10696090012788773,
-0.0960487574338913,
-0.011004485189914703,
-0.12590067088603973,
-0.0387069471180439,
0.0487111434340477,
0.02103874832391739,
-0.02211388200521469,
-0.03549269586801529,
-0.010385602712631226,
0.003951164428144693,
-0.038424570113420486,
0.03660372272133827,
-0.0006... | 0.016372 |
is being guided by virtual agents. ### Local Development Set Up This application is designed to run on port 8080. Upon launch, the application will initialize and bind to port 8080, making it accessible for incoming connections. This can be changed in the .env file. #### Protocol Buffer Compiler: This implementation le... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ccai-agentassist-five9-grpc/README.md | main | gcp-professional-services | [
-0.06538166850805283,
-0.0385475680232048,
-0.014640823937952518,
-0.158565491437912,
-0.02669464610517025,
-0.057993266731500626,
-0.04928538203239441,
-0.009568383917212486,
-0.0396595261991024,
-0.0009363498538732529,
-0.005012195557355881,
-0.026914069429039955,
-0.0708291307091713,
0.... | 0.002766 |
# TensorFlow Profiling Examples Before launching training job, please copy the raw data and define the environmental variables (the bucket for staging and the bucket where you are going to store data as well as training job's outputs) `export BUCKET=YOUR\_BUCKET gcloud storage cp gs://cloud-training-demos/babyweight/pr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/tensorflow-profiling-examples/README.md | main | gcp-professional-services | [
-0.029027177020907402,
-0.0478937067091465,
-0.05693266913294792,
0.07005088776350021,
0.036543089896440506,
0.04731401801109314,
-0.03995825722813606,
-0.024107933044433594,
-0.09977026283740997,
-0.033005762845277786,
-0.052417296916246414,
-0.1251802146434784,
-0.021173834800720215,
-0.... | -0.010083 |
dumps locally: `rm -rf /tmp/profiler mkdir -p /tmp/profiler gcloud storage cp --recursive $OUTDIR/$MODEL/profiler /tmp` 4. Launch the profiler with `python ui.py --profile\_context\_path=/tmp/profiler/$(ls /tmp/profiler/ | head -1)` | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/tensorflow-profiling-examples/README.md | main | gcp-professional-services | [
0.002804955467581749,
0.015306040644645691,
-0.010122500360012054,
-0.030038969591259956,
0.05886027589440346,
-0.08502195030450821,
-0.01531562302261591,
0.06913258880376816,
-0.01618477702140808,
-0.012148741632699966,
0.04534435644745827,
-0.07312148809432983,
0.05459114909172058,
0.022... | -0.114073 |
# Modern CI/CD with Anthos: Demo Guide ## Overview This guide walks you through putting together a [modern CI/CD reference architecture with Anthos](https://cloud.google.com/solutions/modern-ci-cd-with-anthos). There are different permutations to leverage anthos for your particular use case. The purpose of this guide i... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/README.md | main | gcp-professional-services | [
-0.10121011734008789,
-0.005794638302177191,
0.014874960295855999,
-0.06618046015501022,
-0.08408000320196152,
-0.11530429124832153,
-0.017399216070771217,
0.011189850978553295,
-0.0644783228635788,
0.10371091961860657,
0.02942747063934803,
-0.04091167822480202,
0.06591574102640152,
-0.058... | 0.076752 |
# Prerequisites ## Google Cloud Platform This tutorial uses Anthos which is on the Google Cloud Platform (GCP). If you don’t have an account, you can [sign up](https://cloud.google.com/free/) for $300 in free credits. Estimated cost to run this tutorial is $9 per day. After signing up, [create](https://cloud.google.com... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/1-prerequisites.md | main | gcp-professional-services | [
-0.0946783795952797,
-0.03129446879029274,
0.014993710443377495,
-0.04546573758125305,
-0.03993222489953041,
-0.06841069459915161,
-0.004349593538790941,
0.029691647738218307,
-0.004694181028753519,
0.08491451293230057,
-0.021896030753850937,
-0.03987519070506096,
0.09664211422204971,
-0.0... | -0.006479 |
# Set up Anthos Config Management (ACM) [Anthos Config Management](https://cloud.google.com/anthos/config-management) (ACM) is a key component of Anthos that lets you define and enforce configs, including custom policies, and apply it across all your infrastructure both on-premises and in the cloud. With ACM, you can s... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/3-set-up-anthos-config-management.md | main | gcp-professional-services | [
0.010623653419315815,
-0.04705417901277542,
-0.018763920292258263,
-0.0182436965405941,
0.012102249078452587,
-0.021231038495898247,
0.023641612380743027,
-0.052911821752786636,
-0.042570650577545166,
0.06236632913351059,
0.0345478318631649,
0.006746192462742329,
-0.004506201948970556,
-0.... | 0.073841 |
configmanagement.gke.io/cluster-selector: dev-cluster-selector EOF cd .. mkdir stage cd stage cat > namespace.yaml << EOF apiVersion: v1 kind: Namespace metadata: name: stage annotations: configmanagement.gke.io/cluster-selector: dev-cluster-selector EOF cd .. mkdir prod cd prod cat > namespace.yaml << EOF apiVersion: ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/3-set-up-anthos-config-management.md | main | gcp-professional-services | [
0.0017636261181905866,
0.02539592795073986,
-0.029846733435988426,
-0.03983764350414276,
0.0677514597773552,
0.03786864131689072,
0.08921545743942261,
-0.04150361567735672,
-0.07061109691858292,
0.08428077399730682,
0.0417512021958828,
-0.11021020263433456,
0.04652324318885803,
-0.03889613... | 0.101434 |
# Register Clusters with Anthos ## Create GKE clusters For this tutorial we’ll create 2 GKE clusters called `dev` and `prod`. The dev cluster will be used for the dev and test environments while the prod cluster will be used for the prod environment. Create dev and prod clusters: ```bash for i in "dev" "prod"; do gclou... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/2-register-gke-clusters-with-anthos.md | main | gcp-professional-services | [
0.049307748675346375,
-0.047386035323143005,
-0.0687374547123909,
-0.03231474384665489,
0.03739931434392929,
-0.03713495284318924,
0.04171903431415558,
-0.0046973987482488155,
-0.020929288119077682,
0.07990868389606476,
-0.01908337138593197,
-0.12796005606651306,
0.02596954070031643,
0.014... | 0.064587 |
# CICD with Anthos In this section, we’ll automate a CI/CD pipeline taking advantage of the features from anthos. ## Create app Before creating a CICD pipeline we need an application. For this tutorial, we’ll use the popular hello kubernetes application created by paulbower but with a few modifications. Download hello-... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
-0.052788566797971725,
-0.02760905958712101,
-0.027967626228928566,
-0.07362626492977142,
-0.05203859508037567,
-0.10008737444877625,
0.013229488395154476,
0.056223493069410324,
0.04746002331376076,
0.07961796969175339,
0.011824787594377995,
-0.06604786217212677,
0.011103333905339241,
-0.0... | 0.133751 |
- namePattern: gcr.io/kaniko-project/\* - namePattern: gcr.io/cloud-solutions-images/kustomize:3.7 - namePattern: gcr.io/kpt-functions/gatekeeper-validate - namePattern: gcr.io/kpt-functions/read-yaml - namePattern: gcr.io/stackdriver-prometheus/\* - namePattern: gcr.io/$PROJECT\_ID/cloudbuild-attestor - namePattern: g... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
-0.035164013504981995,
-0.011428999714553356,
-0.04473958909511566,
-0.009620538912713528,
0.07597294449806213,
-0.05658145993947983,
0.02221459150314331,
-0.06196611747145653,
0.008574721403419971,
0.0660879984498024,
0.05639547109603882,
-0.13600802421569824,
-0.04870516434311867,
0.0018... | 0.166529 |
image: gcr.io/cloud-solutions-images/kustomize:3.7 tags: - prod only: refs: - main script: - DIGEST=\$(cat images/digest.txt) # build out staging manifests - mkdir -p ./hydrated-manifests/ # stage - cd \${KUSTOMIZATION\_PATH\_NON\_PROD} - kustomize edit set image app=\${HOSTNAME}/\${PROJECT\_ID}/\${CONTAINER\_NAME}@\${... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
0.03869766741991043,
0.04754255339503288,
0.05722158029675484,
-0.04189937934279442,
0.03824497014284134,
-0.040125712752342224,
0.0414883978664875,
0.03281271457672119,
0.027270428836345673,
0.028903091326355934,
0.0028559528291225433,
-0.10890986025333405,
0.00867137685418129,
0.01564513... | 0.066657 |
\${CI\_PIPELINE\_URL}" git push origin stage fi EOF ``` Push platform-admin remote: In gitlab, create a blank public project under the [$GROUP\_NAME](https://gitlab.com/dashboard/groups) group called `platform-admin` then run the following commands to push `platform-admin` dir to gitlab ```bash cd ~/$GROUP\_NAME/platfo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
-0.010379143990576267,
-0.037740614265203476,
-0.039150670170784,
-0.048888519406318665,
-0.04827123507857323,
-0.0978299081325531,
-0.008192076347768307,
0.02488839440047741,
0.0311542097479105,
0.04792051762342453,
0.03003503940999508,
-0.06980007886886597,
0.07618904858827591,
0.0162176... | -0.069928 |
## Register gitlab runner [Gitlab runner](https://docs.gitlab.com/runner/) is what runs the jobs in the gitlab pipeline. In this tutorial, we'll install the Gitlab runner application to our prod cluster and register it as our gitlab runner. Before registering gitlab runner on our system, we’ll enable[ workload identity... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
-0.07050399482250214,
-0.01515432819724083,
0.011393417604267597,
-0.030642731115221977,
-0.032228171825408936,
-0.05615914613008499,
0.01484904158860445,
-0.035483431071043015,
0.04336225613951683,
0.016476795077323914,
-0.0566406287252903,
-0.044902969151735306,
0.04756232723593712,
-0.0... | 0.001737 |
namespace.yaml << EOF apiVersion: v1 kind: Namespace metadata: name: gitlab EOF cd .. cat > service-account.yaml << EOF apiVersion: v1 kind: ServiceAccount metadata: name: gitlab-runner annotations: iam.gke.io/gcp-service-account: gitlab-sa@$PROJECT\_ID.iam.gserviceaccount.com EOF ``` Push changes to acm repo: ```bash ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/docs/4-cicd-with-anthos-and-gitlab.md | main | gcp-professional-services | [
-0.022156229242682457,
-0.04194736108183861,
-0.034165672957897186,
0.004060035105794668,
-0.027796654030680656,
-0.043432462960481644,
0.03240012750029564,
0.003081226721405983,
0.07145998626947403,
0.05659836530685425,
0.02045402117073536,
-0.09642720222473145,
0.07325540482997894,
-0.04... | 0.034608 |
# Hello Kubernetes! This container image can be deployed on a Kubernetes cluster. When accessed via a web browser on port `8080`, it will display: - a default \*\*Hello world!\*\* message - the pod name - node os information  The default "Hello world!... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/src/hello-kubernetes/README.md | main | gcp-professional-services | [
0.008225617930293083,
0.09247521311044693,
0.03705250099301338,
-0.056183964014053345,
-0.00062830705428496,
-0.06903651356697083,
-0.028306666761636734,
-0.02260446734726429,
0.020992478355765343,
0.010951961390674114,
0.013252973556518555,
-0.03286823257803917,
0.020034784451127052,
-0.0... | 0.041354 |
`app` folder in the VS Code Remote Containers terminal, you will be able to access the website on `http://localhost:8080`. You can change the port in the `.devcontainer\devcontainer.json` file under the `appPort` key. See [here](https://code.visualstudio.com/docs/remote/containers) for more details on working with this... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-cicd-with-gitlab/src/hello-kubernetes/README.md | main | gcp-professional-services | [
0.036804862320423126,
-0.009634800255298615,
-0.0045663462951779366,
-0.006722116377204657,
0.013817639090120792,
-0.03020954504609108,
-0.09251190721988678,
0.06636179238557816,
0.007903847843408585,
0.05095653235912323,
-0.010685877874493599,
0.0012305878335610032,
-0.016298962756991386,
... | -0.047746 |
This is an example of running build steps with cloudbuild, having docker-compose running HTTPS\_PROXY in the background connected to a bastion host via Identity Aware Proxy using 'gcloud compute ssh'. This is one of the way to restrict outbound public IP address of the cloudbuild default pool. # Prerequisite: ## Setup ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-with-tcp-proxy/README.md | main | gcp-professional-services | [
0.0038670306093990803,
0.07493487745523453,
0.040025509893894196,
-0.04398332163691521,
-0.04007330909371376,
0.013708259910345078,
-0.03216591104865074,
-0.07595387101173401,
0.004063720814883709,
0.05936186760663986,
-0.015387464314699173,
-0.0654173418879509,
0.07489851862192154,
-0.018... | -0.125676 |
# Composer Dependency Management ### TL;DR: This repository presents a Cloud Composer workflow designed to orchestrate complex task dependencies within Apache Airflow. The solution specifically addresses the challenge of managing parent-child DAG relationships across varying temporal frequencies (yearly, monthly, weekl... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-composer-dependency-management-example/README.md | main | gcp-professional-services | [
-0.11363695561885834,
-0.017165595665574074,
0.005158910062164068,
0.026971466839313507,
0.026809511706233025,
-0.07739248871803284,
-0.010723407380282879,
-0.013865943066775799,
0.01922553777694702,
-0.04897531121969223,
-0.04098285734653473,
-0.06340048462152481,
-0.04875127598643303,
-0... | 0.09717 |
a series of interconnected data pipelines, each designed to perform specific tasks and produce valuable insights. #### Yearly Refresh: Company Calendar Once a year, Symphony Goods executed a critical process known as ["Company\_cal\_refresh"](company\_cal\_refresh.py). This workflow ensured that the company's internal ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-composer-dependency-management-example/README.md | main | gcp-professional-services | [
-0.09713505953550339,
0.008172580972313881,
-0.005559041630476713,
0.024640515446662903,
-0.027459580451250076,
0.0032023685052990913,
-0.04702538996934891,
-0.0007937554619275033,
-0.029855012893676758,
-0.0476253367960453,
-0.014728834852576256,
0.020952163264155388,
0.0010904660448431969,... | 0.240293 |
to drive business growth. | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-composer-dependency-management-example/README.md | main | gcp-professional-services | [
0.008070966228842735,
0.046023570001125336,
0.028568457812070847,
-0.005799546837806702,
0.0009021288715302944,
0.04218205437064171,
-0.009263236075639725,
-0.000331302871927619,
0.08095521479845047,
-0.0015651517314836383,
0.024377714842557907,
0.11609330773353577,
-0.04201654717326164,
0... | 0.260868 |
# Cloud Run CRL Monitoring This project deploys a serverless solution to monitor Certificate Revocation Lists (CRLs) using Google Cloud Run, Cloud Scheduler, and Cloud Monitoring. It validates that CRLs are: 1. \*\*Accessible\*\*: Can be downloaded via HTTP/HTTPS. 2. \*\*Valid\*\*: Are in valid DER or PEM format. 3. \*... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudrun-crl-monitor/README.md | main | gcp-professional-services | [
-0.11820987612009048,
0.01887786202132702,
-0.0008626578492112458,
-0.026364952325820923,
-0.0023493370972573757,
-0.06518718600273132,
0.009469040669500828,
-0.07242550700902939,
0.0775744840502739,
0.023487824946641922,
-0.0012349518947303295,
-0.059799790382385254,
0.07259026914834976,
... | 0.002777 |
# Better Consumer Complaint and Support Request Handling With AI ## Contributors - Dimos Christopoulos (Google) - [Shane Kok](https://www.linkedin.com/in/shane-kok-b1970a82/) (shanekok9@gmail.com) - Andrew Leach (Google) - Anastasiia Manokhina (Google) - [Karan Palsani](https://www.linkedin.com/in/karanpalsani/) (karan... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-support-routing/README.md | main | gcp-professional-services | [
-0.17664547264575958,
-0.05781613662838936,
-0.0063437120988965034,
0.05478418618440628,
0.027034439146518707,
0.04939834028482437,
0.0188024640083313,
0.030703039839863777,
-0.03768705949187279,
-0.00702508632093668,
0.008565237745642662,
-0.08604329824447632,
0.02936698868870735,
-0.0325... | 0.147021 |
tested these instructions in other environments. \*\*All commands, unless otherwise stated, should be run from the directory containing this README.\*\* ## Enable Required APIs in your Project These instructions have been tested in a fresh Google Cloud project without any organization constraints. You should be able to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-support-routing/README.md | main | gcp-professional-services | [
-0.0710861012339592,
-0.044481873512268066,
-0.00014256460417527705,
-0.08210518211126328,
-0.026987051591277122,
-0.011321566067636013,
-0.039087045937776566,
-0.06413038820028305,
-0.06903626769781113,
0.03773981332778931,
0.01550817210227251,
-0.06915783137083054,
0.040190424770116806,
... | -0.069868 |
and if you aren't rerunning the model build you don't need to change `global.dataset\_display\_name` and `global.model\_display\_name`. If you need to change the default paths (because you are running somewhere besides an AI Platform Notebook, because your repo is in a different path, or because your AutoML service acc... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-support-routing/README.md | main | gcp-professional-services | [
-0.0022974046878516674,
-0.03623273968696594,
0.051261261105537415,
0.023946518078446388,
-0.05987783148884773,
-0.00631271256133914,
0.0026627418119460344,
-0.004266960080713034,
-0.04414133355021477,
0.048724520951509476,
0.020389650017023087,
-0.11702385544776917,
0.04149702191352844,
0... | 0.015557 |
default is 1, but you should expect an extra hour of spin up time on top of the training budget. Upping the budget may improve model performance. ## Online Predictions The example pipeline makes batch predictions, but a common deployment pattern is to create an API endpoint that receives features and returns a predicti... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-support-routing/README.md | main | gcp-professional-services | [
0.00652303546667099,
-0.05208912119269371,
-0.05115976557135582,
0.034001823514699936,
0.019249800592660904,
-0.04944337531924248,
-0.0860975831747055,
0.06308309733867645,
-0.07097061723470688,
-0.015922803431749344,
-0.039242345839738846,
-0.03583446145057678,
-0.019702816382050514,
-0.0... | 0.017098 |
# Storage Transfer Service(STS) Metrics [Google Cloud STS](https://cloud.google.com/storage-transfer/docs/overview) provides options to move data between buckets or from other cloud providers. Users can schedule recurring or one-time STS jobs to move data for data backup, synchronization, and replication. As a managed ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/sts-metrics/README.md | main | gcp-professional-services | [
-0.06532861292362213,
-0.08636680245399475,
-0.005838251672685146,
0.003444698639214039,
-0.05468469858169556,
-0.021112214773893356,
0.07271067798137665,
-0.0170359555631876,
0.0660315528512001,
0.016242869198322296,
-0.008981415070593357,
-0.03742734715342522,
0.058704640716314316,
0.050... | 0.13426 |
log like the following: > [INFO ] 2022-12-18 11:33:06.250 [pool-7-thread-1] StsJobHelper - Creating one time transfer job in STS: {description=eshen-test, name=transferJobs/a-eshen-1671391986249, notificationConfig={payloadFormat=JSON, pubsubTopic=projects/eshen-test-3/topics/eshen-sts-metrics-topic}, projectId=eshen-t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/sts-metrics/README.md | main | gcp-professional-services | [
-0.028077568858861923,
0.0025477197486907244,
0.006824004463851452,
0.04380915313959122,
0.01687506027519703,
-0.07109882682561874,
0.05928861349821091,
-0.06385451555252075,
0.07074660062789917,
0.040935106575489044,
-0.05136999115347862,
-0.12376739084720612,
0.003479425795376301,
0.0389... | 0.133807 |
# Energy Price Forecasting Example This repository contains example code to forecast energy prices. Specifically, given a historical time series of hourly spot prices and weather, it predicts future hourly spot prices multiple days into the future. The code takes in raw data from BigQuery, transforms and prepares the d... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-energy-price-forecasting/README.md | main | gcp-professional-services | [
-0.09146218746900558,
0.0002846652932930738,
0.053290147334337234,
0.08718924969434738,
0.04209108650684357,
-0.11878383904695511,
-0.02866741269826889,
0.0011475355131551623,
-0.058803439140319824,
0.02149982936680317,
-0.0681111216545105,
-0.05019056424498558,
-0.006225191988050938,
-0.0... | -0.024619 |
# Running a GKE cluster with control plane authority features In GKE, Google Cloud fully manages the security configuration of the control plane, including \*\*encryption of storage at rest\*\*, and configuring the keys and certificate authorities (CAs) that sign and verify credentials in your clusters. The control pla... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gke-control-plane-authority/README.md | main | gcp-professional-services | [
-0.04270615801215172,
-0.020749304443597794,
0.012946195900440216,
-0.005099076312035322,
-0.0025730656925588846,
-0.009234484285116196,
0.014460149221122265,
-0.09243721514940262,
-0.04616245627403259,
0.04210038110613823,
-0.0015164228389039636,
-0.039408616721630096,
0.06154752895236015,
... | 0.02922 |
the GKE cluster and KMS keys within the same GCP Project. \* Ensure the required variables are populated in a ```terraform.tfvars``` file or passed in as arguments in the apply command. Sample variables file: ``` project\_id = "my-gcp-project" org\_id = "1234567890" cluster\_name = "cpa-cluster" network\_project\_id = ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gke-control-plane-authority/README.md | main | gcp-professional-services | [
-0.0017799873603507876,
-0.0018340253736823797,
0.023898344486951828,
-0.057900551706552505,
-0.06831854581832886,
-0.025834903120994568,
0.022520767524838448,
-0.03886587917804718,
0.03151363506913185,
0.070042185485363,
-0.01847095414996147,
-0.16127067804336548,
0.07601499557495117,
-0.... | 0.045595 |
You can serve your TensorFlow models on Google Kubernetes Engine with [TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving). This example illustrates how to automate deployment of your trained models to GKE. In production setup, it's also useful to load test your models to tune TensorFlow Serving configura... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/tf-load-testing/README.md | main | gcp-professional-services | [
0.0076932236552238464,
-0.09719964861869812,
0.03092939779162407,
-0.0417134165763855,
-0.05129023641347885,
-0.014966408722102642,
-0.0535297654569149,
-0.025750141590833664,
-0.039807114750146866,
-0.015888329595327377,
-0.06641191244125366,
-0.06841004639863968,
0.027153192088007927,
-0... | 0.068454 |
gcr.io/mogr-test-277422/tensorflow-app:latest env: - name: MODEL\_NAME value: regression ports: - containerPort: 8500 - containerPort: 8501 args: ["--model\_config\_file=/benchmark/models.config", "--tensorflow\_intra\_op\_parallelism=4", "--tensorflow\_inter\_op\_parallelism=4", "--batching\_parameters\_file=/benchmar... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/tf-load-testing/README.md | main | gcp-professional-services | [
-0.04180961474776268,
-0.03830833360552788,
-0.05385163798928261,
0.05502679944038391,
-0.01558363251388073,
-0.07346321642398834,
-0.047572407871484756,
0.0023050005547702312,
-0.050918467342853546,
-0.09730120003223419,
0.007087971083819866,
-0.029461940750479698,
-0.04424972087144852,
-... | 0.041601 |
## Dataflow pipeline to change the key of a Bigtable For an optimal performance of our requests to a Bigtable instance, [it is crucial to choose a good key for our records](https://cloud.google.com/bigtable/docs/schema-design), so that both read and writes are evenly distributed across the keys space. Although we have ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigtable-change-key/README.md | main | gcp-professional-services | [
-0.0004985698615200818,
-0.02103939838707447,
0.0031292622443288565,
-0.023044146597385406,
-0.039743877947330475,
-0.0923546627163887,
-0.08262684941291809,
0.007920009084045887,
-0.0509442575275898,
0.06322622299194336,
0.01569453999400139,
-0.03319057077169418,
0.07455278933048248,
-0.1... | -0.086164 |
triggering the pipeline. Then run: ```bash # Change if your location is different JAR\_LOC=target/bigtable-change-key-bundled-0.1-SNAPSHOT.jar PROJECT\_ID= REGION= TMP\_GS\_LOCATION= BIGTABLE\_INSTANCE= INPUT\_TABLE= OUTPUT\_TABLE= RUNNER=DataflowRunner java -cp ${JAR\_LOC} com.google.cloud.pso.pipeline.BigtableChangeK... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigtable-change-key/README.md | main | gcp-professional-services | [
0.010090353898704052,
-0.023623937740921974,
0.025479383766651154,
-0.040665846318006516,
0.003129251068457961,
-0.017830587923526764,
-0.07592711597681046,
0.0040000611916184425,
-0.0443824864923954,
0.02873940020799637,
-0.03619897738099098,
-0.06959950923919678,
0.012331841513514519,
-0... | -0.051087 |
CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigtable-change-key/README.md | main | gcp-professional-services | [
-0.017406092956662178,
0.037016917020082474,
-0.05546785518527031,
-0.04463174566626549,
-0.02692166157066822,
0.05144880339503288,
0.01092204824090004,
-0.044430073350667953,
0.02712048403918743,
-0.037726618349552155,
0.041487619280815125,
-0.01703323982656002,
0.034683987498283386,
0.06... | 0.135405 |
# BigQuery Audit Log Dashboard This example shows you how to build a dashboard using Data Studio for visualization and a SQL script to query the back-end data source. The dashboard displays different metrics pertaining to BigQuery consumption. The purpose of the dashboard is to be used as a BigQuery auditing tool that ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/README.md | main | gcp-professional-services | [
-0.010753239504992962,
0.0266435407102108,
-0.06762705743312836,
0.011837264522910118,
0.04838644340634346,
-0.05588741600513458,
0.020632345229387283,
0.0003697971405927092,
-0.011011547408998013,
0.04482031613588333,
-0.08250890672206879,
-0.009798262268304825,
0.020708724856376648,
-0.0... | 0.046988 |
the data source created in the step 3 above. Click on create report. Rename the report (dashboard) to a name of your choice. | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/README.md | main | gcp-professional-services | [
-0.019336402416229248,
-0.002470494480803609,
-0.1058204397559166,
0.04388854652643204,
0.0178181491792202,
0.03563804551959038,
-0.03913033753633499,
0.017952801659703255,
0.026335088536143303,
0.051410965621471405,
0.010829743929207325,
-0.04915611818432808,
0.09240879863500595,
-0.00166... | 0.017988 |
# Load Jobs Report This document outlines the Load Jobs report (page 2) of the dashboard and explains the various graphs and tables present on the page. #### Note: In all further sections, the "time", "week" or "day" is relative to the timeframe selected in the date filter in the Selection Bar at the top of the page ##... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/docs/load_jobs.md | main | gcp-professional-services | [
-0.062286537140607834,
0.04722762107849121,
-0.020448723807930946,
0.09148065745830536,
0.05619033798575401,
-0.0304995309561491,
-0.009439361281692982,
-0.004246730823069811,
-0.036662034690380096,
0.011842516250908375,
-0.05973313748836517,
0.004617944825440645,
0.0529865138232708,
0.034... | 0.065171 |
# Extract Jobs Report This document outlines the Extract Jobs report (page 3) of the dashboard and explains the various graphs and tables present on the page. #### Note: In all further sections, the "time", "week" or "day" is relative to the timeframe selected in the date filter in the Selection Bar at the top of the p... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/docs/extract_jobs.md | main | gcp-professional-services | [
-0.10802905261516571,
0.05910846218466759,
-0.0150609090924263,
0.08195163309574127,
0.12467515468597412,
0.008117863908410072,
0.006303072907030582,
-0.01411046739667654,
-0.03542790189385414,
0.017140213400125504,
-0.023435022681951523,
-0.008724185638129711,
0.03889594227075577,
0.05059... | 0.074154 |
# Queries Report This document outlines the Queries report (page 5) of the dashboard and explains the various graphs and tables present on the page. #### Note: In all further sections, the "time", "week" or "day" is relative to the timeframe selected in the date filter in the Selection Bar at the top of the page ### Se... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/docs/query_jobs.md | main | gcp-professional-services | [
-0.07134354114532471,
0.06711261719465256,
0.0009291566093452275,
0.11018778383731842,
0.05369257926940918,
-0.009928814135491848,
0.002052862197160721,
0.0014878205256536603,
-0.02657218649983406,
-0.006762745324522257,
-0.05317261442542076,
-0.041788775473833084,
0.07112617790699005,
0.0... | 0.063988 |
# Copy Jobs Report This document outlines the Copy Jobs report (page 4) of the dashboard and explains the various graphs and tables present on the page. #### Note: In all further sections, the "time", "week" or "day" is relative to the timeframe selected in the date filter in the Selection Bar at the top of the page ##... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/docs/copy_jobs.md | main | gcp-professional-services | [
-0.07601282745599747,
0.035654813051223755,
-0.01971161738038063,
0.055513326078653336,
0.08771036565303802,
0.02422953024506569,
0.014051415026187897,
-0.013728497549891472,
-0.054297324270009995,
0.01065222080796957,
-0.014722615480422974,
-0.019787216559052467,
0.06767961382865906,
0.04... | 0.048559 |
# Overall Usage Report This document outlines the Overall Usage Report (page 1) of the dashboard and explains the various graphs and tables present on the page. #### Note: In all further sections, the "time", "week" or "day" is relative to the timeframe selected in the date filter present in the Selection Bar at the to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-audit-log/docs/overall_usage.md | main | gcp-professional-services | [
-0.011717593297362328,
0.032788511365652084,
-0.03737654536962509,
0.11407103389501572,
0.04755398631095886,
-0.04257592558860779,
0.009285488165915012,
0.023143094033002853,
-0.017371509224176407,
0.027449117973446846,
-0.05305952951312065,
-0.007805262226611376,
0.005477153230458498,
0.0... | 0.091254 |
# Dataproc Running Notebooks ## Objective Orchestrator to run Notebooks on an Ephemeral Dataproc cluster via Cloud Composer ## File Directory Structure ├── composer\_input │ ├── initialization\_scripts/ init\_pip\_gcsfuse.sh │ ├── jobs/ wrapper\_papermill.py │ ├── DAGs/ composer\_pyspark\_notebook.py ├── notebooks │ ├─... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-running-notebooks/README.md | main | gcp-professional-services | [
-0.10792769491672516,
-0.018522504717111588,
-0.023065418004989624,
0.022884661331772804,
-0.0038127677980810404,
-0.03178669139742851,
-0.04766407608985901,
0.03949049115180969,
-0.03585640713572502,
0.04898484796285629,
0.01118266861885786,
-0.07197339087724686,
0.013539022766053677,
-0.... | -0.034282 |
# CloudSQL Custom Metric This example demonstrates how to create a custom metric for Stackdriver Monitoring. This example estimates the number of IP's consumed in a CloudSQL private services subnet.  ## Component Description A Stackdriver log sink at the orga... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudsql-custom-metric/README.md | main | gcp-professional-services | [
-0.010262669064104557,
-0.06461530178785324,
-0.004560625180602074,
0.07117240130901337,
-0.0473022498190403,
-0.034872300922870636,
0.032222334295511246,
-0.03455427661538124,
0.1067686453461647,
0.04419415816664696,
-0.05522829294204712,
-0.09145299345254898,
0.05930899828672409,
-0.0321... | 0.049035 |
# Overview Google Cloud Storage (GCS) provides several server side encryption features such as [CMEK](https://cloud.google.com/kms/docs/cmek) and [CSEK](https://cloud.google.com/storage/docs/encryption/customer-supplied-keys) besides the standard [Google managed](https://cloud.google.com/kms/docs/cmek#default-encryptio... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-client-encrypt/README.md | main | gcp-professional-services | [
-0.09282607585191727,
0.011987528763711452,
0.04156496003270149,
-0.004520883783698082,
-0.04615307226777077,
0.029171358793973923,
0.052907489240169525,
-0.04010775312781334,
0.053214967250823975,
0.07596417516469955,
-0.023330090567469597,
0.04075774550437927,
0.044192615896463394,
-0.09... | -0.089141 |
to store the docker images of client application and mitm proxy. - Run the following commands to build and push the docker images to the artifact registry for both client application and mitm proxy. (Note: The Dockerfile are present in both `/app` and `/mitm` directory to build the docker images): ```bash cd # Change d... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-client-encrypt/README.md | main | gcp-professional-services | [
-0.036890797317028046,
0.014482256025075912,
0.027392299845814705,
-0.056413572281599045,
-0.06770755350589752,
-0.038899850100278854,
-0.04368865489959717,
0.02738890051841736,
0.010657627135515213,
0.06835593283176422,
-0.03206682205200195,
-0.05400799959897995,
0.0635094940662384,
-0.01... | -0.047224 |
# BigQuery DDL Validator A utility that will read the Legacy DDL and compare it against the previously extracted DDL and produce an output with the name of the objects where the DDL is no longer matching. ## Business Requirements Most often, migration projects take many months. At each period, groups of objects are mig... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/Readme.md | main | gcp-professional-services | [
-0.023146698251366615,
-0.013619410805404186,
0.02262321673333645,
-0.07521454989910126,
0.026171009987592697,
-0.08756442368030548,
-0.03795396909117699,
-0.041567761451005936,
-0.05245748162269592,
0.0075395237654447556,
-0.024815857410430908,
-0.0067309183068573475,
-0.01325717568397522,
... | 0.015454 |
requirements.txt using pip3. `pip3 install -r requirements.txt ` 3. Download the key from gcloud or console and save in a trusted folder. 4. Add the required inputs for infrastructure setup in the `tfvars` file in `iac` folder. Then run the following commands :- ``` terraform init terraform plan terraform apply ``` 4. ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/Readme.md | main | gcp-professional-services | [
0.03685256093740463,
-0.006257874425500631,
0.0006317516090348363,
-0.03935246169567108,
-0.050353169441223145,
-0.013576914556324482,
-0.01426177378743887,
-0.01185695081949234,
-0.03488970920443535,
0.05486094206571579,
-0.06587158888578415,
-0.1662009209394455,
0.04059726744890213,
-0.0... | -0.01456 |
# Teradata BQ DDL Connector Connector to access the Teradata Metastore Data. In teradata, the DDL information is stored in `TablesV` table with information like `TableKind`, `lastaltertime` and `lastalter\_user`. There are 2 columns stored in the metastore table recording the Last DDL Time and user who executed the las... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/plugin/teradata/teradata.md | main | gcp-professional-services | [
0.023826997727155685,
0.0005977128748781979,
-0.06070898100733757,
-0.005150041077286005,
-0.08224870264530182,
-0.008033907040953636,
0.011740700341761112,
0.03519924357533455,
-0.0039138165302574635,
0.0384933277964592,
0.056409720331430435,
-0.06198453530669212,
-0.0018560608150437474,
... | 0.003098 |
# Oracle BQ DDL Connector Connector to access the Oracle Metastore Data. In Oracle, the metadata information is stored in `all\_objects` table inside the database. There is a column stored in the metastore table recording the Last DDL Time. 1. `last\_ddl\_time` The plugin extracts these value from `owner` which is the ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/plugin/oracle/oracle.md | main | gcp-professional-services | [
-0.011328616179525852,
-0.03166554123163223,
-0.10513918101787567,
-0.008747714571654797,
-0.04375997558236122,
-0.05108682066202164,
0.03853197023272514,
0.07469628751277924,
-0.02571767382323742,
0.032527852803468704,
0.0490463487803936,
-0.029527349397540092,
-0.038964882493019104,
-0.0... | -0.028251 |
# Snowflake BQ DDL Connector Connector to access the Snowflake Metastore Data. In snowflake, the DDL information is stored in `information\_schema.tables` for table, `information\_schema.procedures` for procedures and `information\_schema.views` for views. There are 2 columns stored in the metastore table recording the... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/plugin/snowflake/snowflake.md | main | gcp-professional-services | [
0.03669532388448715,
-0.023220889270305634,
-0.05245250090956688,
0.021207863464951515,
-0.008920412510633469,
-0.018959321081638336,
-0.005222896579653025,
0.054167356342077255,
0.015619750134646893,
-0.011599491350352764,
-0.009249674156308174,
-0.02934076078236103,
0.0027799371164292097,
... | -0.063953 |
# Hive BQ DDL Connector Connector to access the Hive Metastore Data. The Connector assumes that the metastore is stored in Cloud SQL instance. There are 2 columns stored in the metastore table recording the Last DDL Time and user who executed the last modification. 1. `transient\_lastDdlTime` 2. `last\_alter\_user` The... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/plugin/hive/hive.md | main | gcp-professional-services | [
0.02223934978246689,
-0.06083773449063301,
-0.0250608678907156,
0.020747100934386253,
-0.02568855695426464,
0.005287132225930691,
0.03595459833741188,
0.01294678170233965,
-0.033352188766002655,
0.09530025720596313,
0.06115306541323662,
-0.029035551473498344,
0.004339207895100117,
-0.03352... | -0.059652 |
# BQ DDL Infrastructure Setup The following module sets up the infrastructure required for BQ DDL Validator. This includes Cloud SQL Instance and the audit databases. To set up the infrastructure on your end, set the following variable values in tfvars file. The `database\_type` can be `h` for hive, `t` for teradata, `... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ddl-validator/iac/Readme.md | main | gcp-professional-services | [
0.02949024736881256,
-0.02150147594511509,
-0.08199550211429596,
-0.03621656447649002,
-0.08364195376634598,
-0.014464196749031544,
0.07245032489299774,
0.02592758648097515,
-0.028361495584249496,
0.05281630903482437,
-0.048979099839925766,
-0.16751627624034882,
0.12272661924362183,
0.0341... | -0.061342 |
# Basic Python Continuous Integration (CI) With Cloud Source Repositories (CSR) and Google Cloud Build ## Overview This repo contains example code and instructions that show you how to use CSR and Google Cloud Build to automatically run unit tests and pylint upon code check-in. By following this tutorial you will learn... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/python-cicd-with-cloudbuilder/README.md | main | gcp-professional-services | [
-0.07966180145740509,
-0.03970545902848244,
0.0168403722345829,
0.020703623071312904,
-0.04934440180659294,
-0.07480072975158691,
-0.05774242430925369,
-0.036980997771024704,
-0.08076917380094528,
-0.003980701323598623,
-0.00301995687186718,
-0.06463541090488434,
0.09137699753046036,
-0.02... | -0.054775 |
like this, showing successful tests: ```bash $ python3 -m pytest --cov=my\_module tests/ ============================================================================= test session starts ============================================================================== platform darwin -- Python 3.7.3, pytest-4.6.2, py-1.8.... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/python-cicd-with-cloudbuilder/README.md | main | gcp-professional-services | [
-0.0004019079788122326,
0.037518057972192764,
-0.08391854912042618,
-0.036803312599658966,
0.012049957178533077,
-0.0749974325299263,
-0.03072371520102024,
0.031030800193548203,
-0.008701449260115623,
0.013134717009961605,
0.06490213423967361,
-0.07107360661029816,
0.03665033355355263,
0.0... | 0.004697 |
and used Google Cloud Build to create build trigger that runs whenever you push new code. In this last step you'll tie this all together and tell Google Cloud Build how to automatically run tests and run pylint to examine your code whenever a code change is pushed into CSR. In order to tell Google Cloud Builder how to ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/python-cicd-with-cloudbuilder/README.md | main | gcp-professional-services | [
-0.09124323725700378,
-0.013486557640135288,
0.037585657089948654,
0.026298487558960915,
0.005524891894310713,
-0.037474095821380615,
-0.0691409632563591,
-0.079923614859581,
-0.042603351175785065,
-0.008711155503988266,
0.014873302541673183,
-0.08299268037080765,
0.05068424716591835,
-0.0... | -0.116278 |
# CryptoRealTime ## A Google Cloud Dataflow/Cloud Bigtable Websockets example The last year has been like a roller coaster for the cryptocurrency market. At the end of 2017, the value of bitcoin (BTC) almost reached $20,000 USD, only to fall below $4,000 USD a few months later. What if there is a pattern in the high vo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cryptorealtime/README.md | main | gcp-professional-services | [
-0.011840119026601315,
-0.07626067847013474,
-0.00030560343293473125,
-0.006378578022122383,
0.018009016290307045,
-0.06872009485960007,
-0.08161216974258423,
-0.02261282503604889,
0.012693991884589195,
-0.0304087083786726,
-0.04640024155378342,
-0.0589967779815197,
-0.051994286477565765,
... | 0.082559 |
$(gcloud dataflow jobs list \ --format='value(id)' \ --filter="name:runthepipeline\*") ``` \* Empty and Delete the bucket: ```console gcloud storage rm --recursive gs://realtimecrypto-${PROJECT}/\* gcloud storage buckets delete gs://realtimecrypto-${PROJECT} ``` \* Delete the Cloud Bigtable instance: ```console gcloud ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cryptorealtime/README.md | main | gcp-professional-services | [
-0.038617391139268875,
0.016203822568058968,
-0.014066537842154503,
-0.04099427908658981,
0.06206539645791054,
-0.07716234773397446,
0.025610247626900673,
-0.054330624639987946,
0.08680418878793716,
0.03076465055346489,
-0.03309440612792969,
-0.040809325873851776,
0.022505810484290123,
-0.... | -0.042032 |
# Terraform template ### Get the BTC-USD realtime multi exchange observer running in less than 10 minutes  ### Terraform version used in this tutorial: v0.11.13 ### Providers used: - provider.google v2.2.0 - provider.template v2.1.0 ### Setup: - Open the Terraform Shell and clone th... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cryptorealtime/TERRAFORM-README.md | main | gcp-professional-services | [
-0.02003307081758976,
-0.0017201098380610347,
0.0476674810051918,
-0.030257610604166985,
0.011465443298220634,
-0.052648626267910004,
-0.04092225804924965,
-0.04532049968838692,
-0.0014851788291707635,
0.06974154710769653,
-0.024187972769141197,
-0.11546716094017029,
0.002513899700716138,
... | -0.030965 |
# Dialogflow CX - Private Webhook example Simple Dialogflow CX webhook for demo and troubleshooting purpose. Solution based on [`FastAPI`](https://fastapi.tiangolo.com/). Example designed to implement a Dialogflow CX webhook using [Service Directory for private network access](https://cloud.google.com/dialogflow/cx/doc... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dialogflowcx-private-webhook-example/README.md | main | gcp-professional-services | [
-0.10779472440481186,
0.09197772294282913,
-0.002155220601707697,
-0.0765874832868576,
-0.05300631746649742,
-0.12891234457492828,
-0.02455277182161808,
0.008293635211884975,
0.039820168167352676,
-0.061181798577308655,
-0.02680831402540207,
-0.15064005553722382,
0.04446073994040489,
-0.01... | 0.035949 |
# Table Access Pattern Analysis This module consists of deep dive analysis of a BigQuery environment in Google Cloud Platform, according to audit logs - data access data, which can be used to optimise BigQuery usage, and improve time, space and cost of BigQuery. ## Pipeline Optimisation #### **Definitions** The word 'p... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
-0.038626279681921005,
0.014540834352374077,
-0.026565037667751312,
0.0025908530224114656,
0.03169410675764084,
-0.03359350934624672,
0.07885471731424332,
-0.046664539724588394,
-0.006935896817594767,
0.004839648958295584,
0.04653731361031532,
-0.006735545117408037,
0.03776216879487038,
-0... | 0.002368 |
represents a pipeline from one table to another. The weight of the edges indicates the frequency of jobs of that pipeline compared to the rest of the pipelines in the current graph. #### **Analysing the Result** As can be seen from the GIF, the tool will visualise all the pipelines associated with a table. To be specif... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
-0.038634322583675385,
-0.019018780440092087,
-0.07210816442966461,
0.04364892095327377,
0.01142509188503027,
-0.0776837095618248,
0.04132615774869919,
-0.04126887768507004,
0.004767833277583122,
0.03911557421088219,
-0.04224326089024544,
-0.056920938193798065,
0.05637100711464882,
-0.0270... | 0.148884 |
├── README.md │ ├── pipeline.ipynb │ ├── pipeline-output\_only.ipynb │ ├── requirements.txt │ └── var.env ``` There are several subdirectories under the `table-access-pattern-analysis` subdirectory. * **assets/** This directory contains images or other assets that are used in README.md* **bq\_routines/** This directory... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
-0.0048952847719192505,
0.0019022099440917373,
-0.03571734577417374,
0.016167795285582542,
0.04280053824186325,
-0.060754820704460144,
-0.05998809635639191,
0.023515429347753525,
-0.02684505097568035,
0.009150582365691662,
-0.042749375104904175,
0.06768277287483215,
-0.022654863074421883,
... | -0.047339 |
wildcard, include the wildcard in the INPUT\_AUDIT\_LOGS\_TABLE\_ID variable as well.+ Example values \* INPUT\_PROJECT\_ID = 'project-a' \* INPUT\_DATASET\_ID = 'dataset-b' \* INPUT\_AUDIT\_LOGS\_TABLE\_ID = 'cloudaudit\_googleapis\_com\_data\_access\_\*'* **OUTPUT\_PROJECT\_ID, OUTPUT\_DATASET\_ID** + Definition \* T... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
0.03742998093366623,
0.00227418914437294,
-0.02234763465821743,
0.010922830551862717,
0.004670124500989914,
-0.051154762506484985,
0.05930349975824356,
-0.03149222582578659,
-0.05369601771235466,
0.0369257852435112,
0.008628449402749538,
-0.11572916805744171,
0.07621893286705017,
-0.054038... | -0.032117 |
logs data on 5th May 2021 to 6th May 2021. 4. Run pipeline optimisation analysis produced in Jupyter Notebook * **Pipeline Optimisation, run `pipeline.ipynb`** This tool helps identify pipeline optimisation points. At first, the tool will list down tables with high difference of writing and reading frequency throughout... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
-0.005872219335287809,
0.027802390977740288,
-0.020503144711256027,
-0.00010807020589709282,
-0.009790884330868721,
-0.06565724313259125,
-0.061600346118211746,
-0.017037151381373405,
-0.04651764780282974,
0.010258332826197147,
-0.03588829189538956,
-0.014732837677001953,
0.00437747780233621... | 0.074389 |
"NULLABLE", "description": "The destination table" }, { "name": "sourceTable", "type": "STRING", "mode": "NULLABLE", "description": "The source table" }, { "name": "pipelineId", "type": "INTEGER", "mode": "NULLABLE", "description": "The pipeline ID for the pipeline that this pair was part of" } ] ```* table\_direct\_pi... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-table-access-pattern-analysis/README.md | main | gcp-professional-services | [
0.017346469685435295,
0.033606138080358505,
-0.06611546128988266,
0.017048411071300507,
-0.034417517483234406,
-0.012483097612857819,
0.047720275819301605,
0.019225187599658966,
-0.08999693393707275,
0.0029743348713964224,
-0.02950809709727764,
-0.09249310940504074,
-0.015171030536293983,
... | 0.055296 |
# [Neo4j](https://neo4j.com/developer/graph-database/) Backup & Restore via [GKE Cronjob](https://cloud.google.com/kubernetes-engine/docs/how-to/cronjobs) and [GCS](https://cloud.google.com/storage) Example ## Project Structure ``` . └── neo4j\_backup\_restore\_via\_gke\_gcs\_example └── backup └── deployment ├── backu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/neo4j-backup-restore-via-gke-gcs-example/README.md | main | gcp-professional-services | [
-0.03853623941540718,
0.03777098283171654,
0.006268712691962719,
-0.05230071395635605,
0.0007867916719987988,
-0.00648508220911026,
-0.02597215585410595,
-0.014354588463902473,
-0.028655292466282845,
0.08162844181060791,
-0.016138780862092972,
-0.025566784664988518,
0.06204080581665039,
-0... | 0.097587 |
# Google Distributed Cloud Edge (GDCE) Terraform Example This example demonstrates how to provision Google Distributed Cloud Edge (GDCE) resources using terraform with Cloud Build. The resources provisioned are listed below: - GDCE Cluster - GDCE Node Pool - GDCE VPN Connection ## Run build using Cloud Build ### Requir... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gdce-terraform-example/README.md | main | gcp-professional-services | [
-0.03952289745211601,
-0.05618894845247269,
0.022646469995379448,
0.004791628569364548,
-0.004425596445798874,
-0.02511199377477169,
-0.04700435698032379,
-0.04873766005039215,
-0.03754911944270134,
0.11655405163764954,
0.012766058556735516,
-0.12100439518690109,
0.025930004194378853,
-0.0... | -0.046486 |
# Cloud AgentSpace and Discovery API with Domain-Wide Delegation (DWD) This project demonstrates how to authenticate and interact with Google Cloud's AgentSpace and Discovery Engine API using Domain-Wide Delegation (DWD) to impersonate a user. This approach is particularly useful when you need to perform actions on beh... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-agentspace-wif-dwd/README.md | main | gcp-professional-services | [
-0.1661192774772644,
-0.011903844773769379,
0.03500780463218689,
-0.07441741228103638,
-0.0010956067126244307,
-0.0709722489118576,
0.06602441519498825,
-0.044860366731882095,
0.007236869540065527,
-0.01412016898393631,
-0.024871446192264557,
-0.044949598610401154,
0.08368996530771255,
-0.... | 0.093108 |
token and constructs OAuth 2.0 credentials that can be used for authentication. 4. \*\*Search Execution:\*\* Finally, the `search()` method uses these credentials to instantiate a `SearchServiceClient`. This client is then used to execute a search request against the configured Discovery Engine. ## Building and Running... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-agentspace-wif-dwd/README.md | main | gcp-professional-services | [
-0.053763628005981445,
0.009453794918954372,
-0.05429425090551376,
-0.051024939864873886,
-0.0553608275949955,
-0.05616914853453636,
-0.004790823440998793,
0.04227996990084648,
0.006877414882183075,
-0.015539065934717655,
0.010581848211586475,
-0.03057749755680561,
0.13206079602241516,
-0.... | 0.011491 |
# GCP Sample Logs ## Overview This is a sample repository of GCP Audit Logs intended to help Operations and Security teams understand the structure and fields of logs for a variety of services. Each log file contains the log event, a brief description of the event, and the Cloud Logging query used to find events of tha... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/audit-log-examples/README.md | main | gcp-professional-services | [
-0.03255428001284599,
-0.0017271031392738223,
0.06279081851243973,
0.0013061071513220668,
0.07893180102109909,
-0.03338613361120224,
0.13551843166351318,
-0.08058798313140869,
0.03851592540740967,
0.06962133944034576,
0.014507913030683994,
-0.062113888561725616,
0.014404918998479843,
0.012... | 0.032921 |
# Cloud Run to BQ Sample This sample shows how to deploy a application to Cloud Run which gets the data over REST API and inserts to BQ. ## Prerequisites \* A project \* A service account which needs to be associated with cloud run with below permissions on project \* roles/bigquery.dataEditor \* roles/logging.logWrite... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudrun-to-bq/README.md | main | gcp-professional-services | [
0.0035325500648468733,
0.007906690239906311,
-0.03901804983615875,
-0.03769571706652641,
-0.09197118133306503,
-0.0005926315789110959,
-0.034035034477710724,
0.022918568924069405,
-0.021280182525515556,
0.08999978005886078,
-0.031057802960276604,
-0.1224031001329422,
0.1104697436094284,
-0... | -0.088737 |
# billboard If you are cost conscious and need to control your environment, then you can use this to understand your billing Billboard can be easily setup with use of provided template, Cloud users are being empowered to add and modify visuals per customer specifics as needed, 1. For optimizations 2. For noticing anoma... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/billboard/README.md | main | gcp-professional-services | [
-0.0029401241336017847,
-0.0007381222094409168,
0.029843168333172798,
0.008196445181965828,
0.06743557006120682,
0.031134603545069695,
-0.017257580533623695,
-0.010757576674222946,
-0.0020050080493092537,
0.02910560555756092,
-0.023922672495245934,
-0.08229083567857742,
0.06375394761562347,
... | -0.044348 |
# Billboard Overview This code implements billboard dataset using standard and detailed billing exports and creates necessary BQ views. Datastudio template report/dashboard is used to show prebuilt reports based on the BQ views. ## Environment set-up You can set-up the right python environment as follows: ``` cd exampl... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/billboard/billboard-walkthrough.md | main | gcp-professional-services | [
-0.005206832196563482,
0.05307265743613243,
-0.06842470914125443,
0.004912819247692823,
0.043305084109306335,
-0.012235649861395359,
-0.05198215693235397,
0.05640922114253044,
-0.0494430810213089,
-0.0463065579533577,
-0.011917737312614918,
-0.10048287361860275,
0.04475947096943855,
-0.013... | 0.015633 |
## Dataflow Python Flex Template This example contains a sample Dataflow job which reads a XML file and inserts the records to BQ table. It explains how to create Flex template and run it in a restricted environment where there is no internet connectivity to dataflow launcher or worker nodes. It also run dataflow templ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-flex-python/README.md | main | gcp-professional-services | [
-0.08347000926733017,
0.00095392856746912,
-0.06102694198489189,
0.025883518159389496,
0.0030917490366846323,
-0.00780322402715683,
-0.035196226090192795,
0.03407430648803711,
0.01793232560157776,
-0.01956663280725479,
-0.02202647365629673,
-0.019736772403120995,
0.020849118009209633,
-0.1... | 0.069191 |
--member="serviceAccount:dataflow-worker-sa@$PROJECT\_ID.iam.gserviceaccount.com" --role="roles/storage.legacyBucketWriter" ``` #### Create BQ Dataset ``` bq --location=$LOCATION mk --dataset $PROJECT\_ID:$BQ\_DATASET ``` #### Create ARtifactory registry ``` gcloud artifacts repositories create $REPO --location $LOCATI... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-flex-python/README.md | main | gcp-professional-services | [
-0.04752921313047409,
0.010738776065409184,
0.030704131349921227,
-0.028475474566221237,
-0.009749305434525013,
-0.023183118551969528,
0.04744306951761246,
-0.035557229071855545,
0.023282209411263466,
0.06058523431420326,
-0.007605527527630329,
-0.1169373169541359,
0.037755995988845825,
-0... | -0.037055 |
500mb. This is if you go with the default worker. \*\*Contributors:\*\* @singhpradeepk, @kkulczak, @akolkiewicz \*\*Credit:\*\* Sample data has been borrowed from https://learn.microsoft.com/en-in/dotnet/standard/linq/sample-xml-file-customers-orders-namespace#customersordersinnamespacexml Data Model has been borrowed ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-flex-python/README.md | main | gcp-professional-services | [
-0.027787771075963974,
0.0032239544671028852,
-0.035219065845012665,
0.010530284605920315,
-0.042378127574920654,
-0.02184300124645233,
-0.01587946154177189,
-0.004812285304069519,
-0.021808244287967682,
0.06881702691316605,
0.04355471953749657,
0.04420386627316475,
0.018759313970804214,
-... | 0.067818 |
``` Copyright 2023 Google. This software is provided as-is, without warranty or representation for any use or purpose. Your use of it is subject to your agreement with Google. ``` ## Technology Stack - Google Cloud Run - Google Artifact Registry - Google Cloud Storage - Google Speech to Text - Vertex AI Conversation - ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/genai-gradio-example/README.md | main | gcp-professional-services | [
-0.10199016332626343,
-0.030984986573457718,
0.020626449957489967,
-0.09122733771800995,
-0.021547401323914528,
-0.011732393875718117,
0.03319849818944931,
-0.017095452174544334,
0.005159943364560604,
0.058378592133522034,
-0.03416657820343971,
-0.07616591453552246,
0.050104714930057526,
-... | 0.056765 |
``` Copyright 2023 Google. This software is provided as-is, without warranty or representation for any use or purpose. Your use of it is subject to your agreement with Google. ``` # Running voice-activated chatBot on Linux # Enable APIs on GCP The following is a list of the APIs that need to be enabled in GCP - Speech-... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/genai-gradio-example/frontend/README.md | main | gcp-professional-services | [
-0.07879363000392914,
-0.1351381242275238,
0.0348069965839386,
-0.10113100707530975,
-0.04688043147325516,
-0.029008617624640465,
-0.02112221159040928,
-0.06322669237852097,
-0.0389399528503418,
-0.030194660648703575,
-0.0008616620325483382,
-0.036932505667209625,
-0.08780429512262344,
-0.... | 0.001464 |
``` Copyright 2023 Google. This software is provided as-is, without warranty or representation for any use or purpose. Your use of it is subject to your agreement with Google. ``` # LLM Middleware The Service calls the Gen AI agent to get response from Large language Models over Dialogflow CX APIs. ## Library Install t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/genai-gradio-example/llm-middleware/README.md | main | gcp-professional-services | [
-0.06465267390012741,
-0.044930025935173035,
0.07294076681137085,
-0.09349936246871948,
-0.06280943006277084,
-0.05143938586115837,
0.052263084799051285,
-0.008215369656682014,
0.04495514929294586,
-0.02699524536728859,
-0.08131915330886841,
-0.0748073011636734,
0.019524268805980682,
-0.01... | 0.017838 |
# Near realtime (NRT) Feature Producer ## Hypothetical Scenario We want to build and use near real time (NRT) features in the hypotethical scoring system. Scoring is not part of this example. There are multiple sources that produce NRT features. Features are ideally defined in the feature store system and are exposed i... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-near-realtime-feature-engineering/README.md | main | gcp-professional-services | [
-0.03714030608534813,
-0.007816427387297153,
-0.06923899054527283,
0.09924290329217911,
0.044486574828624725,
0.061442647129297256,
-0.03039248287677765,
0.04890666529536247,
-0.06798828393220901,
-0.06820479035377502,
-0.020712604746222496,
-0.07186999171972275,
0.0037374745588749647,
0.0... | 0.116588 |
``` ┌──────────────┐ │ PubSubIO │ Topic: taxirides-realtime │ (Read/Source)│ └──────┬───────┘ │ PCollection v ┌────────────────┐ │ JsonToRow │ └──────┬────┬────┘ │ │ PCollection │ │ │ │ │ │ ┌───────┘ │ │ └────────┐ v v ┌──────────────┐ ┌───────────────────┐ │NRTFeature │ │ NRT Feature (pax) │ max(passenger\_count) grou... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-near-realtime-feature-engineering/README.md | main | gcp-professional-services | [
-0.04703992232680321,
-0.07952773571014404,
0.007395213004201651,
-0.030355170369148254,
-0.04964335262775421,
-0.025998234748840332,
0.02997197024524212,
0.02460363693535328,
-0.06326691806316376,
-0.06183305010199547,
0.04861466586589813,
-0.07981384545564651,
0.034118179231882095,
-0.04... | -0.002588 |
# XML to BQ via Cloud DataFlow This example contains a Dataflow pipeline to read nested XML data from Google Cloud Storage and write to BigQuery while maintaining the nested structure of the XML data in BQ via STRUCT fields. The example utilizes Beam's XMLIO to read XML input into a PCollection, creates BQ TableRow obj... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-xmlio-to-bq/README.md | main | gcp-professional-services | [
0.021396702155470848,
0.006902938708662987,
-0.000567674171179533,
-0.0025530820712447166,
-0.08592389523983002,
0.02238377183675766,
-0.052706267684698105,
0.02534826099872589,
-0.045249465852975845,
-0.00139975908678025,
-0.02635977976024151,
-0.09958403557538986,
0.07024122774600983,
-0... | -0.037072 |
# Stream Spanner Data Changes to BigQuery This repo uses terraform to create below resources in order to deploy an end-to-end pipeline for spanner change streams and streaming the observed changes to BigQuery. \* The Cloud Spanner instance to read change streams from. \* The Cloud Spanner database to read change stream... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/spanner-changestreams-bigquery/README.md | main | gcp-professional-services | [
-0.03146393224596977,
-0.015098347328603268,
-0.0017104139551520348,
-0.005243135616183281,
0.01545365247875452,
-0.05215764790773392,
-0.03459392860531807,
-0.06279608607292175,
-0.01207639742642641,
0.07514708489179611,
-0.022386115044355392,
-0.06721583008766174,
0.020323622971773148,
-... | -0.056878 |
## Indexing documents into Elasticsearch using Cloud Dataflow This example Cloud Dataflow pipeline demonstrates the process of reading JSON documents from Cloud Pub/Sub, enhancing the document using metadata stored in Cloud Bigtable and indexing those documents into [Elasticsearch](https://www.elastic.co/). The pipelin... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-elasticsearch-indexer/README.md | main | gcp-professional-services | [
-0.042636364698410034,
0.05477334186434746,
0.03627007082104683,
-0.013281392864882946,
0.0393892303109169,
-0.03871293365955353,
-0.07828691601753235,
-0.04337330535054207,
0.08060724288225174,
0.01724333129823208,
-0.058980997651815414,
-0.0035777618177235126,
0.033523544669151306,
-0.04... | 0.069757 |
the documents will be published. | Field | Value |Example | | :--------------------- |:---------------------------------------------- |:--------------------------- | | addresses | \*comma-separated-es-addresses\* |http://x.x.x.x:9200 | | index | \*es-index-name\* |prod\_index | | type | \*es-index-type\* |prod | 5. Gen... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-elasticsearch-indexer/README.md | main | gcp-professional-services | [
-0.014736860059201717,
0.07927217334508896,
-0.05748337507247925,
-0.007719892542809248,
-0.019786128774285316,
-0.05482109263539314,
-0.0018748766742646694,
0.03484562784433365,
-0.04892684891819954,
0.013486679643392563,
0.02986256033182144,
-0.08369725942611694,
0.027327850461006165,
-0... | 0.095535 |
# BigQuery Remote Function Sample Code [Bigquery remote function](https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions) allows user to deploy their custom services or libraries written in any language other than SQL and javascript, which are not present as bigquery user defined functions. BQ r... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bq-remote-function/README.md | main | gcp-professional-services | [
0.013927352614700794,
-0.0018403582507744431,
0.02086341567337513,
-0.007097254041582346,
-0.04003830626606941,
0.010656344704329967,
-0.07166697829961777,
-0.016438666731119156,
0.033829860389232635,
0.0346636101603508,
-0.023448728024959564,
0.00397925591096282,
-0.0006015729159116745,
-... | -0.099346 |
## Setup service extensions(callout) with GCP Application Load Balancer (EXTERNAL\_MANAGED) ### OVERVIEW: [Callout-based Service Extensions](https://cloud.google.com/service-extensions/docs/overview) let users of Google Cloud products, such as Cloud Load Balancing and Media CDN, to insert programmability directly into ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gclb-callouts/README.md | main | gcp-professional-services | [
-0.09073151648044586,
-0.04756055772304535,
0.02636520005762577,
-0.06032899022102356,
-0.10731619596481323,
-0.07185392081737518,
0.0192121434956789,
0.01773729734122753,
0.006650869734585285,
0.019450148567557335,
-0.06802301108837128,
0.02781776711344719,
0.010265006683766842,
-0.057765... | 0.107694 |
terraform init ``` #### Generate the terraform plan ```bash terraform plan ``` #### Apply the terraform plan to provision all GCP resources. ```bash terraform apply ``` After executing the terraform script, we should have the following GCP resource provisioned GCP application load balancer: \* GCP Application Load Bala... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gclb-callouts/README.md | main | gcp-professional-services | [
-0.07929807156324387,
0.04223616048693657,
0.001261346973478794,
-0.03682924807071686,
-0.029873399063944817,
-0.05611047148704529,
-0.00875724758952856,
-0.039104633033275604,
0.038367319852113724,
0.07271160930395126,
-0.08324649930000305,
-0.05624047666788101,
0.03876206651329994,
-0.03... | 0.011607 |
# Uploading files directly to Google Cloud Storage by using Signed URL This is an architecture for uploading files directly to Google Cloud Storage by using Signed URL. ## Overview This code implements the following architecture:  The characteristic of the architecture is that... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/direct-upload-to-gcs/README.md | main | gcp-professional-services | [
-0.06498867273330688,
0.032080236822366714,
0.00430644117295742,
-0.06497573852539062,
0.054344791918992996,
-0.07224298268556595,
-0.0026501258835196495,
-0.012737130746245384,
0.05335531756281853,
0.06946134567260742,
0.00765414722263813,
0.022766603156924248,
0.07811915129423141,
-0.045... | -0.008599 |
values) if err != nil { return "", err } defer resp.Body.Close() b, err := ioutil.ReadAll(resp.Body) if err != nil { return "", err } return strings.TrimSpace(string(b)), nil } func main() { // Get signed url by requesting API server hosted on App Engine. u, err := getSignedURL(signerUrl, url.Values{"content\_type": {"... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/direct-upload-to-gcs/README.md | main | gcp-professional-services | [
-0.05011049658060074,
0.09825289249420166,
-0.009556806646287441,
-0.006497047375887632,
0.04158739373087883,
-0.16375800967216492,
0.028290778398513794,
0.0882415920495987,
0.03259192034602165,
-0.002731968415901065,
0.010264857672154903,
-0.038478247821331024,
0.03914394602179527,
0.0193... | 0.065777 |
## De-id Pipeline Design Document ### Context #### Objective The DLP De-identification Pipeline aims to identify and anonymize sensitive data stored in BigQuery or Google Cloud Storage (GCS). The pipeline reads data from a source, de-identifies sensitive information using Cloud DLP, and writes the de-identified data to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dlp-deid-pipeline/DOC.md | main | gcp-professional-services | [
-0.0875520184636116,
0.06923367828130722,
0.04903244227170944,
-0.0629451647400856,
0.052316997200250626,
-0.034861207008361816,
0.02200273424386978,
-0.038943953812122345,
0.013816066086292267,
0.018937133252620697,
-0.0030378943774849176,
0.013984252698719501,
0.02730366960167885,
-0.081... | 0.10376 |
for sensitive data. By configuring de-identification settings within a template, a reusable blueprint is established. This eliminates the need for repetitive configuration, allowing de-identification jobs to be executed multiple times with ease. To ensure referential integrity while masking sensitive information, a com... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dlp-deid-pipeline/DOC.md | main | gcp-professional-services | [
-0.15361365675926208,
0.16681315004825592,
-0.009826122783124447,
-0.023892944678664207,
-0.027212215587496758,
0.02107880264520645,
0.05264560505747795,
-0.01815304160118103,
-0.0005919623654335737,
-0.07912982255220413,
0.011173988692462444,
0.050155796110630035,
0.03461642935872078,
-0.... | 0.158443 |
# DLP De-identification Pipeline This Beam pipeline reads data from either Google Cloud Storage (GCS) or BigQuery (BQ), de-identifies sensitive data using DLP, and writes the de-identified data to the corresponding destination in GCS or BQ. The pipeline supports two modes: \* \*\*GCS mode:\*\* For processing files stor... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dlp-deid-pipeline/README.md | main | gcp-professional-services | [
-0.03798595443367958,
0.018384482711553574,
0.0323437936604023,
-0.04357430338859558,
-0.019102293998003006,
-0.048743247985839844,
-0.0031967281829565763,
-0.033181387931108475,
-0.06472556293010712,
0.02945391647517681,
-0.04000325873494148,
-0.011782621033489704,
-0.036743126809597015,
... | -0.060627 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.