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storage rm --recursive gs://$BUCKET\_NAME/transformed-$TIMESTAMP gcloud dataproc jobs submit pyspark --region=$REGION --cluster=$CLUSTER\_NAME-testing-4x4-standard scripts/spark\_average\_speed.py -- gs://$BUCKET\_NAME/raw-$TIMESTAMP/ gs://$BUCKET\_NAME/transformed-$TIMESTAMP/ ``` \*\*2 x n2-standard-8 = 1 min 31 secon... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-job-optimization-guide/README.md | main | gcp-professional-services | [
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clusters (see step 6) to allow clusters to scale up, and delete them when the job or workflow is complete. Downscaling may not be necessary on ephemeral, job/workflow scoped clusters. - Ensure primary workers make up >50% of your cluster. Do not scale primary workers. - This does increase cost versus a smaller number o... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-job-optimization-guide/README.md | main | gcp-professional-services | [
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# Churn Prediction with Survival Analysis This model uses Survival Analysis to classify customers into time-to-churn buckets. The model output can be used to calculate each user's churn score for different durations. The same methodology can be used used to predict customers' total lifetime from their "birth" (initial ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-churn-prediction/README.md | main | gcp-professional-services | [
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.. ``` ## Model Training Model training minimizes the negative of the log likelihood function for a statistical Survival Analysis model with discrete-time intervals. The loss function is based off the paper [A scalable discrete-time survival model for neural networks](https://peerj.com/articles/6257.pdf). For each reco... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-churn-prediction/README.md | main | gcp-professional-services | [
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# Personal Workbench Notebooks Deployer Accurate usage logging and cost allocation is key when you have multiple analytics users sharing data products, that is why you want to ensure analytical users use their own credentials when querying and processing data. Automation in provisioning and decommissioning of analytics... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/personal-workbench-notebooks-deployer/README.md | main | gcp-professional-services | [
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secure URL to JupyterHub to the users. When a user pick a template and create a cluster (or reuse an existing one), JupyterHub redirects the user to Dataproc Notebooks through the Component Gateway. Main components: - \*\*JupyterHub:\*\* UI+web server (GCP managed) for users to pick Jupyter notebooks server templates a... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/personal-workbench-notebooks-deployer/README.md | main | gcp-professional-services | [
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# Personal Managed Workbench notebooks ## Example ```hcl module "sample-managed-module" { source = "./modules/personal-managed-notebook" notebook\_users\_list = ["@"] managed\_instance\_prefix = "" project\_id = "" } ``` ## Variables | name | description | type | required | default | |----------------------------------... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/personal-workbench-notebooks-deployer/modules/personal-managed-notebook/README.md | main | gcp-professional-services | [
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# Personal User Managed Workbench notebooks ## Example ```hcl module "sample-user-managed-module" { source = "./modules/personal-user-managed-notebook" notebook\_users\_list = ["@"] dataproc\_yaml\_template\_file\_name = "" personal\_dataproc\_notebooks\_bucket\_name = "" user\_managed\_instance\_prefix = "" generated\... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/personal-workbench-notebooks-deployer/modules/personal-user-managed-notebook/README.md | main | gcp-professional-services | [
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# BigQuery Group Sync For Row Level Access Sample code to synchronize group membership from G Suite/Cloud Identity into BigQuery and join that with your data to control access at row level. ## Source files \* `group\_sync.py` and `group\_sync\_test.py`: main code and unit tests. \* `auth\_util.py`: utility function to ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-row-access-groups/README.md | main | gcp-professional-services | [
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# Data Format Description Language ([DFDL](https://en.wikipedia.org/wiki/Data\_Format\_Description\_Language)) Processor Example This module is a example how to process a binary using a DFDL definition. The DFDL definitions are stored in a Firestore database. The application send a request with the binary to process to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dfdl-firestore-pubsub-example/README.md | main | gcp-professional-services | [
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A topic to publish the final json output: "data-output-json-topic" 2. A topic to publish the binary to be processed: "data-input-binary-topic" #### Subscription The following subscriptions need to be created: 1. A subscription to pull the binary data: data-input-binary-sub ## Usage ### Initialize the application Refere... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dfdl-firestore-pubsub-example/README.md | main | gcp-professional-services | [
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# QAOA Examples for Max-SAT Problems These are examples of parsing a max-SAT problem in a proprietary format. Problems can be converted into QUBO form as described in [this article, appendix C](https://arxiv.org/pdf/1708.09780.pdf) to be later simulated with [cirq](https://github.com/quantumlib/Cirq/). The maxSAT probl... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/qaoa/README.md | main | gcp-professional-services | [
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## Scheduling Command in GCP using Cloud Run and Cloud Scheduler There are some scenarios where running a command regularly in you environment is necessary without a need of orchestration tool such as Cloud Composer. In this example we schedule a command using Cloud Run and Cloud Scheduler. Once such example is running... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/schedule-cloud-run-jobs/Readme.md | main | gcp-professional-services | [
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The Snowflake DDL Migration Utility does the following functionalities: The script connects to Snowflake Database through the snowflake-python connector The script uses the get\_ddl command to retrieve the table schema information The script produces the "create table" statement using the schema information and store t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-snowflake-tables-migration-utility/README.md | main | gcp-professional-services | [
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The Snowflake BQ Converter Script does the following functionalities 1. The script reads the snowflake ddl files from the specified gcs path (output path of the snowflake\_ddl\_extraction script) 2. The script calls the BigQuery Migration API and converts the ddl to the BigQuery DDL and placed it in the specified gcs p... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-snowflake-tables-migration-utility/DDL_Converter/Snowflake BQ Converter.md | main | gcp-professional-services | [
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The Snowflake DDL Extraction Script does the following functionalities: The script connects to Snowflake Database through the snowflake-python connector The script uses the snowflake get\_ddl command to retieve the table schema information The script produces the "create table" statement using the schema information an... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-snowflake-tables-migration-utility/DDL_Extractor/Snowflake DDL Extraction.md | main | gcp-professional-services | [
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The BQ Table Creator Script does the following functionalities Reads the output sql file created by the snowflake bq converter script The script create the Bigquery Tables in the specified target dataset. The table structure will include source columns, metadata columns and paritioning and clustering info The final DDL... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-snowflake-tables-migration-utility/BQ_Table_Creator/BQ Table Creator.md | main | gcp-professional-services | [
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The Archive DDL Script archive the DDL files created by the scripts (snowflake\_ddl\_extraction.py, snowflake\_bq\_converter.py and archive\_ddl.py) and place the files in the specified archive bucket. Below packages are need to run the script: google-cloud-storage Steps to run this script: 1. Make Sure the pre-requsit... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-snowflake-tables-migration-utility/DDL_Archiver/Archive DDL.md | main | gcp-professional-services | [
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# Cloud Function "Act As" Caller > tl;dr The Terraform project in this repository deploys a single simple Python Cloud Function that executes a simple action against Google Cloud API using identity of the function caller illustrating the full flow described above. --- This example describes one possible solution of red... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-functions-act-as/README.md | main | gcp-professional-services | [
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GitHub Workflow defined in this source repository in the [.github/workflows/call-function.yml](./.github/workflows/call-function.yml) file. It authenticates to GCP as a Workload Identity using Workflow Identity Fedederation set up by the Terraform project in this repository. The Service Account "mapped" to the Workload... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-functions-act-as/README.md | main | gcp-professional-services | [
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account \* Workload Identity Service Account – the GCP service account that represents the external GitHub Workload Identity. When the GitHub workflow authenticates to the GCP it is this service account's IAM permissions that the GitHub Workload Identity is granted. | Service Account | Role | Description | |-----------... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-functions-act-as/README.md | main | gcp-professional-services | [
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GitHub [workflow](.github/workflows/call-function.yml) in this repository illustrates the way of calling the sample Cloud Function from a GitHub workflow. For the workflow to succeed, a dedicated service account `wi-sample-account` is mapped to the authenticated GitHub Workload Identity. It needs to have `cloudfunction... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-functions-act-as/README.md | main | gcp-professional-services | [
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run the following command: terraform destroy To delete the project, do the following: 1. In the Cloud Console, go to the [Projects page](https://console.cloud.google.com/iam-admin/projects). 1. In the project list, select the project you want to delete and click \*\*Delete\*\*. 1. In the dialog, type the project ID, an... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-functions-act-as/README.md | main | gcp-professional-services | [
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# Scikit-learn pipeline trainer for AI Platform This is a example for building a scikit-learn-based machine learning pipeline trainer that can be run on AI Platform, which is built on top of the [scikit-learn template](https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/sklearn/sklearn-template/template)... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-sklearn-pipeline/README.md | main | gcp-professional-services | [
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sure the following API & Services are enabled. \* Cloud Storage \* Cloud Machine Learning Engine \* BigQuery API \* Cloud Build API (for CI/CD integration) \* Cloud Source Repositories API (for CI/CD integration) - Configure project id and bucket id as environment variable. ```bash $ export PROJECT\_ID=[your-google-pro... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-sklearn-pipeline/README.md | main | gcp-professional-services | [
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AI Platform. - `hptuning\_config.yaml`: for running hyperparameter tuning job on AI Platform. The YAML files share some configuration parameters. In particular, `runtimeVersion` and `pythonVersion` should correspond in both files. Note that both Python 2.7 and Python 3.5 are supported, but Python 3.5 is the recommended... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-sklearn-pipeline/README.md | main | gcp-professional-services | [
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processing function Args: num: (float) Returns: float """ return np.sqrt(num) def \_numeric\_sq\_sr(num): """Example function that take scala and return an array Args: num: (float) Returns: numpy.array """ return np.array([\_numeric\_square(num), \_numeric\_square\_root(num)]) def \_area(args): """Examples function tha... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-sklearn-pipeline/README.md | main | gcp-professional-services | [
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# Cloud Storage to BigQuery using Cloud Composer End-to-end sample example to do data extraction from Cloud Storage to BigQuery using Composer.git \* Cloud Storage to BigQuery \* With Airflow operator [BigQueryInsertJobOperator](https://airflow.apache.org/docs/apache-airflow-providers-google/stable/\_modules/tests/syst... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-to-bq/README.md | main | gcp-professional-services | [
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Introduction ============ Many consumer-facing applications allow creators to upload audio files as a part of the creative experience. If you’re running an application with a similar use case, you may want to extract the text from the audio file and then classify based on the content. For example, you may want to categ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ml-audio-content-profiling/README.md | main | gcp-professional-services | [
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deploy ```` You will be prompted to use a region to serve the location from. You may pick any region, but you cannot change this value later. You can verify that the app was deployed correctly by navigating to https://`$PROJECT`.appspot.google.com. You should see the following UI:  ##... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ml-audio-content-profiling/README.md | main | gcp-professional-services | [
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yes ```` All of the resources should be deployed. ### View Results ### Test it out 1. You can start by trying to upload an audio file in GCS. You can do this using `gcloud storage` or in the UI under the **staging bucket**. This will trigger `send\_stt\_api\_function`. This submits the request to the Speech API and pub... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ml-audio-content-profiling/README.md | main | gcp-professional-services | [
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# AI-Powered SLO Consultant > \*\*Automated Site Reliability Engineering (SRE) Agent\*\* > > Use your application code to indentify deployment-ready Service Level Objectives (SLOs) through Terraform in minutes using Google Gemini. ## Overview The \*\*AI-Powered SLO Consultant\*\* is an intelligent workflow tool designe... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/slo-assistant/Readme.md | main | gcp-professional-services | [
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test ``` ### Formatting & Linting To ensure code quality and consistency, run `make format` (which uses `black` with a line length of 100) and `make lint` (which uses `pylint`): ```bash # Format code make format # Run linter make lint ``` ## Usage The application uses a decoupled architecture with a FastAPI backend and... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/slo-assistant/Readme.md | main | gcp-professional-services | [
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# Cloud Composer in Shared VPC This repo uses terraform to create below resources in order to deploy a private composer environment in shared VPC. \* Two projects, one for shared VPC and other for composer environment \* One shared VPC and subnets in host project \* Neccesary IAM permissions and firewall rules in order... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/composer-shared-vpc/README.md | main | gcp-professional-services | [
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## Requirements No requirements. ## Providers | Name | Version | |------|---------| | [google](#provider\\_google) | n/a | ## Modules | Name | Source | Version | |------|--------|---------| | [network](#module\\_network) | terraform-google-modules/network/google | ~> 3.4.0 | | [private-dns-zones-shared](#module\\_priva... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/composer-shared-vpc/shared/README.md | main | gcp-professional-services | [
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## Requirements No requirements. ## Providers | Name | Version | |------|---------| | [google](#provider\\_google) | n/a | ## Modules | Name | Source | Version | |------|--------|---------| | [composer-v1-private](#module\\_composer-v1-private) | terraform-google-modules/composer/google//modules/create\_environment\_v1... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/composer-shared-vpc/composer_v1_pvt_shared_vpc/README.md | main | gcp-professional-services | [
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[subnetwork](#input\\_subnetwork) | The subnetwork to host the composer cluster. | `string` | n/a | yes | | [subnetwork\\_region](#input\\_subnetwork\\_region) | The subnetwork region of the shared VPC's host (for shared vpc support) | `string` | `""` | no | | [tags](#input\\_tags) | Tags applied to all nodes. Tags are... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/composer-shared-vpc/composer_v1_pvt_shared_vpc/README.md | main | gcp-professional-services | [
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# dataflow-production-ready (Python) ## Usage ### Creating infrastructure components Prepare the infrastructure (e.g. datasets, tables, etc) needed by the pipeline by referring to the [Terraform module](/terraform/README.MD) Note the BigQuery dataset name that you crate for late steps. ### Creating Python Virtual Envir... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-production-ready/python/README.md | main | gcp-professional-services | [
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to simplify the process of packaging the pipeline into a container we utilize [Google Cloud Build](https://cloud.google.com/cloud-build/). We preinstall all the dependencies needed to \*compile and execute\* the pipeline into a container using a custom [Dockerfile](ml\_preproc/Dockerfile). In this example, we are using... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-production-ready/python/README.md | main | gcp-professional-services | [
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# Ingesting GCS files to BigQuery using Cloud Functions and Serverless Spark In this solution, we build an approch to ingestion flat files (in GCS) to BigQuery using serverless technology. This solution might be not be performanct if you have frequent small files that lands to GCP. We use [Daily Shelter Occupancy](http... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-to-bq-serverless-services/Readme.md | main | gcp-professional-services | [
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<> dataset= <> bq\_table = <> error\_topic=<> ``` - \*\*Step 5:\*\* The cloud function is triggered once the object is copied to bucket. The cloud function triggers the Servereless spark Deploy the function. ``` cd trigger-serverless-spark-fxn gcloud functions deploy trigger-serverless-spark-fxn --entry-point \ invoke\... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-to-bq-serverless-services/Readme.md | main | gcp-professional-services | [
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# Left-Shift Validation at Pre-Commit Hook This pre-commit hook uses open-source tools to provide developers with a way to validate Kubernetes manifests before changes are committed and pushed to a repository. Policy checks are typically instantiated after code is pushed to the repository, as it goes through each envir... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/left-shift-validation-pre-commit-hook/README.md | main | gcp-professional-services | [
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continue the commit. --- ## Purpose Organizations that deploy applications on Kubernetes clusters often use tools like [Open Policy Agent](https://www.openpolicyagent.org/) (OPA) or [Gatekeeper](https://open-policy-agent.github.io/gatekeeper/website/docs) to enforce security and operational policies. These are often es... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/left-shift-validation-pre-commit-hook/README.md | main | gcp-professional-services | [
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- Resetting the default behavior of your pre-commit hook, if you make changes that break the code. - Describing new Constraints and/or ConstraintTemplates to use. We have a collection of samples from the [OPA Gatekeeper Library](https://github.com/open-policy-agent/gatekeeper-library) that you can use, but you can also... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/left-shift-validation-pre-commit-hook/README.md | main | gcp-professional-services | [
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# Monitoring GCP Cloud DNS public zone ## Overview The config files in this repo supports the GCP blogpost on "Visualizing Cloud DNS public zone query data using log-based metrics and Cloud Monitoring". ### Config files included in this repo 1. [config.yaml](config.yaml) 2. [dashboard.json](dashboard.json) 3. [latency-... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-public-zone-dashboard/README.md | main | gcp-professional-services | [
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but also result in ingestion errors. \*\*Example\*\* ``` labelExtractors: ProjectID: EXTRACT(resource.labels.project\_id) QueryName: EXTRACT(jsonPayload.queryName) QueryType: EXTRACT(jsonPayload.queryType) ResponseCode: EXTRACT(jsonPayload.responseCode) TargetName: EXTRACT(resource.labels.target\_name) SourceIP: EXTRAC... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-public-zone-dashboard/README.md | main | gcp-professional-services | [
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# Getting user profile from IAP-enabled GAE application This example demonstrates how to retrieve user profile (e.g. name, photo) from an IAP-enabled GAE application. ## Initial Setup This setup can be done from `Cloud Shell`. You need `Project Owner` permission to run this, e.g. for creating GAE app. The following set... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/iap-user-profile/README.md | main | gcp-professional-services | [
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# mm2-gmk-migration Migrate to Google Managed Kafka using MirrorMaker2 [](https://console.cloud.google.com/cloudshell/editor?cloudshell\_git\_repo=https%3A%2F%2Fgithub.com%2Fmandeeptrehan%2Fmm2-gmk-migration.git) ## Setup Terraform Workspace 1. Upda... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/mm2-gmk-migration/README.md | main | gcp-professional-services | [
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# Hashpipeline ## Overview In this solution, we are trying to create a way to indicate security teams if there is a file found with US Social Security Numbers (SSNs). While the DLP API in GCP offers the ability to look for SSNs, it may not be accurate, especially if there are other items such as account numbers that lo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-dlp-hash-pipeline/README.md | main | gcp-professional-services | [
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a list of valid and random Social Security Numbers \* Store the plain text in `scripts/socials.txt` \* Hash the numbers (normalized without dashes) using HMAC-SHA256 and the key generated from `make create\_key` \* Store the hashed values in Firestore under the collection specified in the terraform variable: `firestore... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-dlp-hash-pipeline/README.md | main | gcp-professional-services | [
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# Data Generator This directory shows a series of pipelines used to generate data in GCS or BigQuery. The intention for these pipelines are to be a tool for partners, customers and SCEs who want to create a dummy dataset that looks like the schema of their actual data in order to run some queries in BigQuery. There are... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-data-generator/README.md | main | gcp-professional-services | [
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using the `--schema\_file` parameter with a file containing a list of json objects with `name`, `type`, `mode` and optionally `description` fields. This form follows the output of`bq show --format=json --schema `. This data generator now supports nested types like `RECORD`/`STRUCT`. Note, that the approach taken was to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-data-generator/README.md | main | gcp-professional-services | [
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use [`bq\_table\_resizer.py`](bigquery-scripts/bq\_table\_resizer.py) to copy the table into itself until it reaches the desired size. ``` --output\_bq\_table=project:dataset.table ``` #### Sparsity (optional) Data is seldom full for every record so you can specify the probability of a NULLABLE column being null with t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-data-generator/README.md | main | gcp-professional-services | [
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to `setup.py`. ### Generating Joinable tables Snowflake schema To generate multiple tables that join based on certain keys, start by generating the central fact table with the above described [`data\_generator\_pipeline.py`](data-generator-pipeline/data\_generator\_pipeline.py). Then use [`data\_generator\_joinable\_ta... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-data-generator/README.md | main | gcp-professional-services | [
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--dataset= \ --table= \ --partitioning\_column date \ --source\_file=files\_to\_load.txt ``` ### BigQuery Histogram Tool This script will create a BigQuery table containing the hashes of the key columns specified as a comma separated list to the `--key\_cols` parameter and the frequency for which that group of key colu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-data-generator/README.md | main | gcp-professional-services | [
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# Cloud Composer Examples This example demonstrates how to test Airflow DAGs using then deploy them to Cloud Composer using with Cloud Build. ## Run build using Cloud Build The included cloudbuild.yaml file has the following flow: 1. Build Airflow DAGs Builder: Builds a docker image with airflow dependencies included 2... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-composer-cicd/README.md | main | gcp-professional-services | [
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# iap-idp-connect ## Introduction This service programmatically connects IAP (Identity Aware Proxy) to IdP (Identity Platform) in Google Cloud Platform. By This program, you connect Identity providers (including multi-tenants) with IAP (Identity Aware Proxy) backend services For example, you connect SAML integrations d... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/iap-idp-connect/README.md | main | gcp-professional-services | [
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# Testing GCS Connector for Dataproc The Google Cloud Storage connector for Hadoop (HCFS) enables running [Apache Hadoop](http://hadoop.apache.org/) or [Apache Spark](http://spark.apache.org/) jobs directly on data in [GCS](https://cloud.google.com/storage) by implementing the Hadoop FileSystem interface. The connector... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-gcs-connector/README.md | main | gcp-professional-services | [
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directory of this project `gcs-connector-poc/`, the script will run the following commands to build the Dataproc cluster using the GCS connector. ```bash cd terraform terraform init terraform apply ``` ### 6. Test the Dataproc cluster The script `test\_gcs\_connector.sh` will test the GCS Connector on your Dataproc clu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-gcs-connector/README.md | main | gcp-professional-services | [
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## Inputs | Name | Description | Type | Default | Required | |------|-------------|------|---------|:-----:| | dataproc\\_cluster | Name for dataproc cluster | `any` | n/a | yes | | dataproc\\_subnet | Name for dataproc subnetwork to create | `any` | n/a | yes | | hadoop\\_version | Hadoop version for the GCS connector... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-gcs-connector/terraform/README.md | main | gcp-professional-services | [
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# Custom Dataproc Scheduled Cluster Deletion This repository provides scripts for launching a Google Cloud Dataproc Cluster while specifying the maximum idle time after which the cluster will be deleted. The custom scripts will consider active SSH sessions and YARN based jobs in determining whether or not the cluster i... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-idle-shutdown/README.md | main | gcp-professional-services | [
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m, h, d” (seconds, minutes, hours, days, respectively). Examples: “30m” or “1d” (30 minutes or 1 day from when the cluster becomes idle). 4. [Optional] Specify, as the value of the metadata key “key\_process\_list”, a semi-colin separated list of process names (in addition to YARN jobs and active SSH connections) for w... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-idle-shutdown/README.md | main | gcp-professional-services | [
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Unit Tests === Run unit tests after installing development dependencis: ```bash pip install -r requirements-dev.txt pytest ``` Save and Replay VM Deletion Events === It is useful to replay VM deletion events to test out changes to the Background Function. See the [Replay Quickstart][replay-qs] for more information. 1. ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/DEVELOPER.md | main | gcp-professional-services | [
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"ip\_address": "", I dns\_vm\_gc 578383257362746 2019-06-12 01:30:09.145 "operation": { I dns\_vm\_gc 578383257362746 2019-06-12 01:30:09.145 "id": "971500189857477422", I dns\_vm\_gc 578383257362746 2019-06-12 01:30:09.145 "name": "operation-1560300992590-58b15e267f2cc-e7529c4d-f1343924", I dns\_vm\_gc 578383257362746... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/DEVELOPER.md | main | gcp-professional-services | [
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2019-06-12 01:30:20.438 "zone": "us-west1-a" I dns\_vm\_gc 578389534045377 2019-06-12 01:30:20.438 }, I dns\_vm\_gc 578389534045377 2019-06-12 01:30:20.438 "type": "gce\_instance" I dns\_vm\_gc 578389534045377 2019-06-12 01:30:20.438 }, I dns\_vm\_gc 578389534045377 2019-06-12 01:30:20.438 "severity": "INFO", I dns\_vm... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/DEVELOPER.md | main | gcp-professional-services | [
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VM DNS Garbage Collection === This folder contains a [Background Function][bg] which deletes DNS A records when a VM is deleted. \*\*Please note\*\* DNS record deletion is implemented, however, cannot be guaranteed. A race exists between the function obtaining the VM IP address and the `compute.instances.delete` operat... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/README.md | main | gcp-professional-services | [
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delete DNS records in the host project. This role may be granted at the Shared VPC project level. Compute Viewer --- Grant the Compute Viewer role to the dns-vm-gc service account. Compute Viewer allows the DNS VM GC function to read the IP address of the VM, necessary to ensure the correct A record is deleted. This ro... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/README.md | main | gcp-professional-services | [
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to day reporting. The correlation is useful for the rare situation of complete end-to-end tracing. Reporting === Lost Race --- Periodic reporting should be performed to monitor for `NOT\_PROCESSED` results. In the event of a lost race, automatic DNS record deletion is not guaranteed. The following Stackdriver Advanced ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcf-pubsub-vm-delete-event-handler/README.md | main | gcp-professional-services | [
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# FinOps Agent 🚀 Welcome to the FinOps Agent repository! This project is a powerful, open-source agent designed to help organizations of all sizes master their cloud spend. It analyzes massive amounts of cloud billing data, identifies waste, and provides actionable optimization recommendations in plain English. This r... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/FinOps-agent/README.md | main | gcp-professional-services | [
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--- ## Sample Data for Quick Start To help you get started quickly without needing live billing data, we have included sample files in the \*\*`synth-data/`\*\* folder of this repository: ### 1. `synth-data/sample\_billing\_data.csv` This file contains 500 rows of simulated, highly varied billing data. You can load thi... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/FinOps-agent/README.md | main | gcp-professional-services | [
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already present in the root of this repository with your own project variables. Here is the template used in the `.env` file: ```env # GCP Project Configuration GOOGLE\_CLOUD\_PROJECT=your-project-id GOOGLE\_CLOUD\_LOCATION=us-central1 # BigQuery Configuration BQ\_DATA\_PROJECT\_ID=your-project-id BQ\_DATASET\_ID=your-... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/FinOps-agent/README.md | main | gcp-professional-services | [
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# Carbon Footprint Dashboard This example shows how to use the prebuilt templates for Carbon Footprint Estimates and create your own Carbon Footprint Dashboard by connecting to carbon reporting exports from BigQuery. # Using Looker Studio Users can use this Data Studio dashboard as-is, or use them as a starting point f... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/carbon-footprint-dashboard/README.md | main | gcp-professional-services | [
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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. # Twilio Conversation Integration with a Virtual Agent using Dialogflow This is an example how to integrate a Twilio Conversation Services with Virt... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ccai-dialogflow-middleware/README.md | main | gcp-professional-services | [
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Set your [Google Application Default Credentials][application-default-credentials] by [initializing the Google Cloud SDK][cloud-sdk-init] with the command: ``` gcloud init ``` Generate a credentials file by running the [application-default login](https://cloud.google.com/sdk/gcloud/reference/auth/application-default/lo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ccai-dialogflow-middleware/README.md | main | gcp-professional-services | [
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to the request so our webhook gets invoked 5) Use the Twilio [Interaction API](https://www.twilio.com/docs/flex/developer/conversations/interactions-api) to invoke a handoff to the Flex UI ## How to run the initializer to programmatically create a Twilio conversation > [ConversationInitializer](./src/main/java/com/midd... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ccai-dialogflow-middleware/README.md | main | gcp-professional-services | [
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# Dataflow Streaming Schema Handler This package contains a set of components required to handle unanticipated incoming streaming data into BigQuery with schema mismatch. The code will uses Schema enforcement and DLT (Dead Letter Table) approach to store schema incompability. In case of schema incompability detected fr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-streaming-schema-handler/README.md | main | gcp-professional-services | [
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# CloudML Marketing (Classification) Model for Banking The goal of this notebook is to create a classification model using CloudML as an alternative to on-premise methods. Along the way you will learn how to store data into BigQuery, fetch and explore that data, understand how to properly partition your dataset, perfor... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudml-bank-marketing/README.md | main | gcp-professional-services | [
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# spanner-interleave-subquery This example contains the benchmark code to examine query efficiency gains of using Cloud Spanner interleaved tables with subqueries. ## Prerequisite Run the following command to create a Cloud Spanner database with [schema.sql](schema.sql). ```bash gcloud spanner databases create ${DATABA... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/spanner-interleave-subquery/README.md | main | gcp-professional-services | [
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# Fixity Metadata for GCS 🗃 This script pulls metadata and checksums for file archives in Google Cloud Storage and stores them in a manifest file and in BigQuery to track changes over time. The script uses the [BagIt](https://tools.ietf.org/html/rfc8493) specification. ## Overview Each time this Fixity function is run... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-fixity-function/README.md | main | gcp-professional-services | [
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# Setup Clone this repository and run locally, or use Cloud Shell to walk through the steps: [](https://ssh.cloud.google.com/cloudshell/open?page=shell&cloudshell\_git\_repo=https://github.com/GoogleCloudPlatform/professional-services&cloudshell\_t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-fixity-function/docs/setup.md | main | gcp-professional-services | [
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# Setup Clone this repository and run locally, or use Cloud Shell to walk through the steps: [](https://ssh.cloud.google.com/cloudshell/open?page=shell&cloudshell\_git\_repo=https://github.com/GoogleCloudPlatform/professional-services&cloudshell\_t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-fixity-function/docs/remove.md | main | gcp-professional-services | [
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# Terraform Config Validator Policy Library This repo contains a library of constraint templates and sample constraints to be used for Terraform resource change requests. If you're looking for the CAI variant, please see [Config Validator](https://github.com/lykaasegura/w-secteam-repo). Everything in this repository ha... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/README.md | main | gcp-professional-services | [
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# This text will be replaced #ENDINLINE ``` Replaced: ``` #INLINE("my\_rule.rego") #contents of my\_rule.rego #ENDINLINE ``` #### Linting Policies Config Validator provides a policy linter. You can invoke it as: ``` go get github.com/GoogleCloudPlatform/config-validator/cmd/policy-tool policy-tool --policies ./policies... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/README.md | main | gcp-professional-services | [
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## Config Validator | Setup & User Guide ### Go from setup to proof-of-concept in under 1 hour \*\*Table of Contents\*\* \* [Overview](#overview) \* [How to set up constraints with Policy Library](#how-to-set-up-constraints-with-policy-library) \* [Get started with the Policy Library repository](#get-started-with-the-p... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/user_guide.md | main | gcp-professional-services | [
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wish to use, create constraint YAML files corresponding to those templates, and place them under `policies/constraints`. Commit the newly created constraint files to \*\*your\*\* Git repository. For example, assuming you have created a Git repository named "policy-library" under your GitHub account, you can use the fol... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/user_guide.md | main | gcp-professional-services | [
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sample IAM domain restriction constraint: ``` cp samples/constraints/iam\_service\_accounts\_only.yaml policies/constraints ``` Let's take a look at this constraint: ``` apiVersion: constraints.gatekeeper.sh/v1beta1 kind: TFGCPIAMAllowedPolicyMemberDomainsConstraintV2 metadata: name: service-accounts-only annotations: ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/user_guide.md | main | gcp-professional-services | [
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# Functional Principles of the Constraint Framework You'll notice that this repository contains a handful of folders, each with different items. It's confusing at first, so let's dive into it! First, let's start with how the library is organized. ## Folder Structure The folder structure below contains a TL;DR explanati... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/functional_principles.md | main | gcp-professional-services | [
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domain(s) you've passed in. You can create multiple constraints for any given ConstraintTemplate, you just need to make sure that the rules don't conflict with one another. For example, any allowlist/denylist policy would be difficult to create multiple constraints for. The reason is that if one constraint is of type `... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/functional_principles.md | main | gcp-professional-services | [
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"\*" } ``` There are two parts to this policy. We have a `violation` object, which contains line-by-line logic statements. The way rego works is that the policy will run line-by-line, and will `break` if any of the conditions don't pass. For instance, if the `resource.type` is \*\*not\*\* "google\_project\_iam\_binding... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/functional_principles.md | main | gcp-professional-services | [
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`mode` and `roles`. If you look at the ConstraintTempalte, you can see that these two fields are defined and described. `Mode` is a string enumerable that \*must\* be either denylist or allowlist. `gcloud beta terraform vet` will actually error out if this is not upheld in the associated constraint(s). ## Additional Re... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-resource-change-policy-library/docs/functional_principles.md | main | gcp-professional-services | [
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# Cloud Support API (v2) Samples ## About the Support API The [Cloud Support API](https://cloud.google.com/support/docs/reference/rest) provides programmatic access to Google Cloud's Support interface. You can use the Cloud Support API to integrate Cloud Customer Care with your organization's customer relationship mana... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-support/appengine_python_example/README.md | main | gcp-professional-services | [
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# java\_working\_app\_example This code is created by Eugene Enclona, If you have any questions about this code reach out to eenclona@google.com ## Overview The purpose of this code is to show how one can build a sample Java app to take advantage of the Cloud Support API. One functionality in this app is to modify the ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-support/java_working_app_example/README.md | main | gcp-professional-services | [
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# Cloud Support API (v2) Samples The [Cloud Support API](https://cloud.google.com/support/docs/reference/rest) provides programmatic access to Google Cloud's Support interface. You can use the Cloud Support API to integrate Cloud Customer Care with your organization's customer relationship management (CRM) system. The ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-support/java_starter_example/README.md | main | gcp-professional-services | [
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## Overview Application Framework to execute various operations on redis to evaluate key performance metrics such as CPU & Memory utilization, Bytes transferred, Time per command etc. For complete list of metrics refer [MemoryStore Redis Metrics](https://cloud.google.com/memorystore/docs/redis/supported-monitoring-metr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/redis-benchmarks/redis-benchmarks/README.md | main | gcp-professional-services | [
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method in [WorkloadExecutor](./src/main/java/com/google/cloud/pso/benchmarks/redis/WorkloadExecutor.java) for reference. ## Disclaimer This project is not an official Google project. It is not supported by Google and disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose. | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/redis-benchmarks/redis-benchmarks/README.md | main | gcp-professional-services | [
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# BigQuery cross-project slots utilization monitoring This solution was written to help monitoring slot utilization across multiple projects, while breaking down allocation per project. This is relevant for customers using [flat-rate pricing](https://cloud.google.com/bigquery/pricing#flat\_rate\_pricing). ## Background... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-cross-project-slot-monitoring/README.md | main | gcp-professional-services | [
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permissions: + Billing Viewer on Billing Account ID ([documentation](https://cloud.google.com/billing/docs/how-to/billing-access#update\_billing\_permissions)). + Monitoring Editor on project hosting Stackdriver account. + Monitoring Viewer on all projects. It will be easier to apply this on Folder / Org level. 6. Metr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-cross-project-slot-monitoring/README.md | main | gcp-professional-services | [
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## python The python folder contains the EphemeralInstance module and associated usage examples. The EphemeralInstance module can be used to deploy and destroy projects, enable services, and bind principles to the deployed projects. Available methods: \* various get methods for project metadata \* deploy\_project() - c... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/ephemeral-projects/README.md | main | gcp-professional-services | [
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This is a collection of Cloud Build Examples for common CICD Tasks to be used for applications where their repository is connected to Google Cloud Build. These `.yaml` examples can be copied to the root directory of applications where their traditional Dockerfiles are or you can choose to add them to a `cloudbuild/`, `... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-application-cicd/README.md | main | gcp-professional-services | [
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# Universal Application Containerizer with Push to Artifact Registry This Example allows you to containerize an application and push to a specific Artifact Registry Docker Repository. Cloud build will containerize the application based on the `Dockerfile` defined within the repository used. Two image tags will be creat... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-application-cicd/containerize/README.md | main | gcp-professional-services | [
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