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# Getting started Copy the `deploy\_cloud\_run.yaml` file into your application's git repository. Modify the substitution default values as needed. # Using the Terraform Example: This Terraform is an example Cloud Build Triggers and some example dependencies that allows you to deploy the latests version of your applica... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-application-cicd/deploy_to_cloud_run/README.md | main | gcp-professional-services | [
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# Internal HTTP Load Balancer Terraform Example This example shows how to deploy an internal HTTP load balancer using plain terraform (i.e. without using any external modules). This example will create all the required resources needed for a working internal load balancer except the GCP project. ## Design In this examp... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/terraform-ilb/README.md | main | gcp-professional-services | [
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# Selective deployment Organizing code across multiple folders within a single version control repositiroy such as github is a very common practice, and we're referring this as multi-folder repository. \*\*Selective deployment\*\* approach lets you find the folders changed within your repository and only run the logic ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-selective-deployment/README.md | main | gcp-professional-services | [
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git clone. Cloud build in its default behaviour uses shallow a copy of the repository (i.e. only the code associated with the commit with which the current build was triggered). Shallow copy prevents us from performing git operations like git diff. However, we can use following step in the cloud build to fetch unshallo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloudbuild-selective-deployment/README.md | main | gcp-professional-services | [
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# ML Ops with Vertex AI for enterprises Enterprises frequently have specific requirements, especially around security and scale, that are often not addressed by other examples. In this example we demonstrate machine learning use case implementation that respects typical security requirements, and that includes that aut... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/README.md | main | gcp-professional-services | [
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# MLOps with Vertex AI ## Set up the experimentation notebook Once the environment has been deployed, the first step is to open the Jupyter notebook available in the [Vertex Workbench section](https://console.cloud.google.com/vertex-ai/workbench/list/managed), under the specific region (e.g. `europe-west4`). Use the `O... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/03-MLOPS.md | main | gcp-professional-services | [
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# MLOps with Vertex AI - Git integration with Cloud Build ## Accessing GitHub from Cloud Build via SSH keys Follow this procedure to create a private SSH key to be used for Github access from Cloud Build: https://cloud.google.com/build/docs/access-github-from-build ``` mkdir workingdir cd workingdir ssh-keygen -t rsa -... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/02-GIT_SETUP.md | main | gcp-professional-services | [
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# Considerations with VPC SC ## Cloud Build Use Cloud Build [private pools](https://cloud.google.com/build/docs/private-pools/using-vpc-service-controls) or create a VPC SC ingress rule or [access level](https://cloud.google.com/access-context-manager/docs/create-basic-access-level#members-example) adding the Cloud Bui... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/VPC-SC.md | main | gcp-professional-services | [
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# Issues when running Github actions: ``` ERROR: (gcloud.builds.submit) There was a problem refreshing your current auth tokens: ('Unable to acquire impersonated credentials', '{\n "error": {\n "code": 403,\n "message": "Permission \'iam.serviceAccounts.getAccessToken\' denied on resource (or it may not exist).",\n "st... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/ISSUES.md | main | gcp-professional-services | [
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# MLOps with Vertex AI - Infra setup ## Introduction This example implements the infrastructure required to deploy an 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. ## GCP resources A t... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/01-ENVIRONMENTS.md | main | gcp-professional-services | [
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should be an existing bucket that your user has access to. - Create a `terraform.tfvars` file and specify the required variables. You can use the `terraform.tfvars.sample` an an starting point ```tfm project\_create = { billing\_account\_id = "000000-123456-123456" parent = "folders/111111111111" } project\_id = "credi... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/doc/01-ENVIRONMENTS.md | main | gcp-professional-services | [
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# Reference KFP Pipeline We include here a reference KFP pipeline implementation, that follows best practices such as: \* Traceability of data by storing training, test and validation datasets as an intermediate artifact \* Splitting the input data into training, test and validation and giving the training step only ac... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/src/kfp_pipelines/README.md | main | gcp-professional-services | [
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that are not included anywhere, and these are to generate a [model card](https://medium.com/google-cloud/build-responsible-models-with-model-cards-and-vertex-ai-pipelines-8cbf451e7632) for our model. A model card, in this case, is an HTML document with any information that we may be required to provide about our model.... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/vertex_mlops_enterprise/src/kfp_pipelines/README.md | main | gcp-professional-services | [
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The Oracle DDL Migration Utility does the following functionalities: 1. The script connects to Oracle Database through the oracle-python connector (oracledb). 2. The script uses the oracle metadata table (all\_tab\_columns) to retrieve the table schema information. 3. The script produces the "create table" statement us... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-oracle-ddl-migration-utility/README.md | main | gcp-professional-services | [
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The Oracle BQ Converter Script does the following functionalities 1. The script reads the oracle ddl files from the specified gcs path (output path of the oracle\_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 path Below... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-oracle-ddl-migration-utility/DDL_Converter/Oracle BQ Converter.md | main | gcp-professional-services | [
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The Oracle DDL Extraction Script does the following functionalities: 1. The script connects to Oracle Database through the oracle-python connector (oracledb) 2. The script uses the oracle metadata table (all\_tab\_columns) to retieve the table schema information 3. The script produces the "create table" statement using... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-oracle-ddl-migration-utility/DDL_Extractor/Oracle DDL Extraction.md | main | gcp-professional-services | [
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The BQ Table Creator Script does the following functionalities 1. Reads the output sql file created by the oracle bq converter script 2. The script create the Bigquery Tables in the specified target dataset. 3. The table structure will include source columns, metadata columns and paritioning and clustering info 3. The ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-oracle-ddl-migration-utility/BQ_Table_Creator/BQ Table Creator.md | main | gcp-professional-services | [
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# Using dbt and Cloud Composer for managing BigQuery example code DBT (Data Building Tool) is a command-line tool that enables data analysts and engineers to transform data in their warehouses simply by writing select statements. Cloud Composer is a fully managed data workflow orchestration service that empowers you to... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dbt-on-cloud-composer/README.md | main | gcp-professional-services | [
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dbt Here are the follow up steps for running the code: 1. Push the code in dbt-project repository and make sure the Cloud Build triggered; and successfully create the docker image 2. In the Cloud Composer UI, run the DAG (e.g dbt\_with\_kubernetes.py) 3. If successfull, check the BigQuery console to check the tables Wi... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dbt-on-cloud-composer/README.md | main | gcp-professional-services | [
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create namespace NAMESPACE ``` 2) Create a Kubernetes service account for your application to use ```bash kubectl create serviceaccount KSA\_NAME \ --namespace NAMESPACE ``` 3) Assuming that the dbt-sa already exists and has the right permissions to trigger BigQuery jobs, the special binding has to be added to allow th... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dbt-on-cloud-composer/README.md | main | gcp-professional-services | [
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## Terraform template to setup the composer environment This terraform template serves as a starting point for the private VPC Composer setup. There are two versions of Cloud Composer configured. The recommended setup should be Composer2 as for its simplicity and its great autoscaling capacity. Create the `terraform.tf... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dbt-on-cloud-composer/terraform/README.md | main | gcp-professional-services | [
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# Kubeflow Fairing Examples `Kubeflow Fairing` is a Python package that streamlines the process of building, training, and deploying machine learning (ML) models in a hybrid cloud environment. By using Kubeflow Fairing and adding a few lines of code, you can run your ML training job locally or in the cloud, directly fr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/kubeflow-fairing-example/README.md | main | gcp-professional-services | [
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tested on notebook service outside Kubeflow cluster also, which means it could be - Notebook running on your personal computer - Notebook on AI Platform, Google Cloud Platform - Essentially notebook on any environment outside Kubeflow cluster For notebook running inside Kubeflow cluster, for example JupytHub will be de... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/kubeflow-fairing-example/README.md | main | gcp-professional-services | [
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# Overview The purpose of this walkthrough is to create [Custom Dataflow templates](https://cloud.google.com/dataflow/docs/concepts/dataflow-templates). The value of Custom Dataflow templates is that it allows us to execute Dataflow jobs without installing any code. This is useful to enable Dataflow execution using an ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/README.md | main | gcp-professional-services | [
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proceed. ``` DIR=infrastructure/03.io terraform -chdir=$DIR init terraform -chdir=$DIR apply -var="project=$(gcloud config get-value project)" ``` ## 6. Provision the Dataflow template builder We will use [Cloud Build](https://cloud.google.com/build) to build the custom Dataflow template. There are advantages to using ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/README.md | main | gcp-professional-services | [
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terraform -chdir=$DIR destroy -var="project=$(gcloud config get-value project)" ``` | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/README.md | main | gcp-professional-services | [
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# Deploy the Custom Dataflow template by following these steps ## Overview The purpose of this walkthrough is to create [Custom Dataflow templates](https://cloud.google.com/dataflow/docs/concepts/dataflow-templates). The value of Custom Dataflow templates is that it allows us to execute Dataflow jobs without installing... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/cloud_shell_tutorial.md | main | gcp-professional-services | [
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own GitHub organization or personal account In order to benefit from [Cloud Build](https://cloud.google.com/build), the service requires we own this repository; it will not work with a any repository, even if it is public. Therefore, complete these steps before proceeding: 1) [Fork the repository](https://github.com/Go... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/cloud_shell_tutorial.md | main | gcp-professional-services | [
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# Overview This directory holds code to build an Apache Beam pipeline written in [python](https://www.python.org/downloads/). # Requirements - [Python version 3.6 or higher](https://www.python.org/downloads/) - [Pip version 7.0.0 or higher](https://python-poetry.org/docs/#installation) # Usage ## Setup virtual environm... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/python/README.md | main | gcp-professional-services | [
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# Overview This directory holds code to build an Apache Beam pipeline written in [java](https://www.java.com/en/). # Requirements \*NOTE: installing gradle is not required\* - [Java version 11](https://www.oracle.com/java/technologies/downloads/#java11) # Usage ## Running Word Count Run the following command to execute... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/java/README.md | main | gcp-professional-services | [
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# Overview This module is responsible for provisioning the builder that builds the custom Dataflow template. This module does not build the template itself but provisions the process, using [Cloud Build](https://cloud.google.com/build) that performs the build step. This module achieves the following: - Provision cloud ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-custom-templates/infrastructure/04.template/README.md | main | gcp-professional-services | [
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# Deploying Redis Cluster on GKE This is a sample K8s configuration files to deploy a Redis Cluster on GKE. ## Pre-requisites - Install Google Cloud SDK, kubectl, and git client. - Enable the Kubernetes Engine API. - Create or select a GCP project. Make sure that billing is enabled for your project. ## How to use 1. Pr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/redis-cluster-gke/README.md | main | gcp-professional-services | [
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# Entities creation and update for Dialogflow This module is an example how to create and update entities for Dialogflow. ## Recommended Reading [Entities Options](https://cloud.google.com/dialogflow/docs/entities-options) ## Technology Stack 1. Cloud Storage 1. Cloud Functions 1. Dialogflow ## Programming Language Pyt... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dialogflow-entities-example/README.md | main | gcp-professional-services | [
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[ "@saving-account-types:saving-account-types" ] }, { "value": "@checking-account-types:checking-account-types", "synonyms": [ "@checking-account-types:checking-account-types" ] }, { "value": "@sys.date-period:date-period @saving-account-types:saving-account-types", "synonyms": [ "@sys.date-period:date-period @saving-a... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dialogflow-entities-example/README.md | main | gcp-professional-services | [
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# Certificate Authority Service Demo This repository contains sample Terraform resource definitions for deploying several related Certificate Authority Service (CAS) resources to the Google Cloud. It demonstrates several Certificate Authority Service features and provides examples of Terraform configuration for the fol... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/certificate-authority-service-hierarchy/README.md | main | gcp-professional-services | [
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$CONCURRENCY $QPS $TIME ``` The test uses [Fortio](https://github.com/fortio/fortio) to call CAS API concurrenly over HTTPS to generate dummy certificates simulataing load on the CA Pool. The test will run for the time duration defined by the `TIME` environment variable. Check the outcome ``` 142.251.39.106:443: 3 172.... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/certificate-authority-service-hierarchy/README.md | main | gcp-professional-services | [
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# Dataflow Streaming Benchmark When developing Dataflow pipelines, it's common to want to benchmark them at a specific QPS using fake or generated data. This pipeline takes in a QPS parameter, a path to a schema file, and generates fake JSON messages matching the schema to a Pub/Sub topic at the rate of the QPS. ## Pip... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-streaming-benchmark/README.md | main | gcp-professional-services | [
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# GCE access to Google AdminSDK ## Introduction This package provides basic instruction and code snippets (in python), to help users manage access to [Google's Admin SDK](https://developers.google.com/admin-sdk/) using GCE's [service account](https://cloud.google.com/compute/docs/access/create-enable-service-accounts-f... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gce-to-adminsdk/README.md | main | gcp-professional-services | [
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## TSOP Log Processor Currently Transfer Service for On Prem writes transfer logs to GCS objects but not to cloud logging. It puts the logs in same bucket where it transfer objects. This cloud function reads logs from GCS object and writes to cloud logging. Cloud Function gets invoked via pub/sub as soon as logs are cr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/tsop-log-processor/README.md | main | gcp-professional-services | [
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## Setup mTLS and TLS with GCP Application Load Balancer (EXTERNAL\_MANAGED) ### OVERVIEW: mTLS with GCP Application Load Balancer enhances security by requiring clients to authenticate themselves with certificates, in addition to the server authenticating itself to the client. This provides mutual authentication, ensu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gclb-mtls-tls/README.md | main | gcp-professional-services | [
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above mtls connection response are populated automatically by load balancer frontend and sent back to the backend server after a successful mTLS connection is established. | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gclb-mtls-tls/README.md | main | gcp-professional-services | [
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# BigQuery to Spanner using Mutations ## Why create this repo? \* When we need to move data from BigQuery to Spanner using Apache Spark in Scala \* \*\*Use of Spanner Mutation instead of SQL\*\* \* Spanner has a limitation on mutations per transaction, particularly affecting its efficiency in handling UPSERTS using [Mu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-spanner/mutations/README.md | main | gcp-professional-services | [
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spark --cluster ${CLUSTER\_NAME} \ --region=us-central1 \ --jar=${GCS\_BUCKET\_JARS}/${APP\_JAR\_NAME} \ --jars=${GCS\_BUCKET\_JARS}/google-cloud-spanner-6.45.1.jar,gs://test-dataproc-spanner/jars/google-cloud-spanner-jdbc-2.17.1-single-jar-with-dependencies.jar \ -- ${PROJECT\_ID} ${BQ\_DATASET} ${BQ\_TABLE} ${SPANNER... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-spanner/mutations/README.md | main | gcp-professional-services | [
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## BigQuery to Spanner using Apache Spark in Scala ### Why create this repo ? \* Google Cloud customers need to move data from BigQuery to Spanner using Apache Spark in Scala \* Dataproc [templates](https://github.com/GoogleCloudPlatform/dataproc-templates) cannot help as they are written in Python & Java \* The Apache... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-spanner/jdbc/README.md | main | gcp-professional-services | [
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list core/project --format="value(core.project)"``` | | BQ\_DATASET | The BigQuery dataset with source data | my\_bq\_dataset | | BQ\_TABLE | The BigQuery table with source data | my\_bq\_table | | BQ\_TABLE\_PK | Primary key column name of the BigQuery table | my\_pk\_column\_name | | SPANNER\_PROJECT\_ID | The ID of ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataproc-spanner/jdbc/README.md | main | gcp-professional-services | [
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# Webhook example This module is a webhook example for Dialogflow. An agent created in Dialogflow is connected to this webhook that is running in Cloud Function. The webhook also connects to a Cloud Firestore to get the users information used in the example. ## Recommended Reading [Dialogflow Fulfillment Overview](http... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dialogflow-webhook-example/README.md | main | gcp-professional-services | [
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a nice day! ### Running the sample from Dialogflow console In [Dialogflow's console](https://console.dialogflow.com), in the simulator on the right, query your Dialogflow agent with `I need assistance` and respond to the questions your Dialogflow agent asks. ### Running the sample using gcloud util Example: $ gcloud fu... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dialogflow-webhook-example/README.md | main | gcp-professional-services | [
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# Multi-regional Application Availability This demo project contains Google Cloud infrastrcuture components that illustrate use cases for enhancing availability of a Cloud Run or Google Cloud Compute Managed Instance Groups based applications. The applciation instances get redundantly deployed to two distinct regions. ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-load-balancing/README.md | main | gcp-professional-services | [
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and running load tests. ## GCE Managed Instance Groups 1. Checkout demo HTTP responder service container ``` git clone https://github.com/GoogleCloudPlatform/golang-samples.git cd golang-samples/run/hello-broken ``` 2. Build container, tag it and push to the Artifact Registry ``` docker build . -t eu.gcr.io/${PROJECT\_... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-load-balancing/README.md | main | gcp-professional-services | [
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the end of the execution should look like following ``` IP addresses distribution: 10.156.0.11:8080: 16 10.199.0.48:8080: 4 Code -1 : 12 (10.0 %) Code 200 : 108 (90.0 %) Response Header Sizes : count 258 avg 390 +/- 0 min 390 max 390 sum 100620 Response Body/Total Sizes : count 258 avg 7759.624 +/- 1.497 min 7758 max 7... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-load-balancing/README.md | main | gcp-professional-services | [
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of external application load balancer with serverless network endpoint groups (NEGs) # is configured with a TLS certificate for the Cloud DNS name resolving to the load balancer IP address, # then the we can also omit `-k` curl parameter and client will verify the server TLS certificate properly: curl "http://metadata.... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-load-balancing/README.md | main | gcp-professional-services | [
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Useful Links \* [Multi-region failover using Cloud DNS Routing Policies and Health Checks for Internal TCP/UDP Load Balancer](https://codelabs.developers.google.com/clouddns-failover-policy-codelab#0) \* [AWS DNS load balancing example](https://docs.aws.amazon.com/whitepapers/latest/real-time-communication-on-aws/cross... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-dns-load-balancing/README.md | main | gcp-professional-services | [
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# dataflow-bigquery-to-alloydb We are going to be moving data from a public dataset stored in BigQuery into a table that will be created in AlloyDB. This is the BigQuery query that will generate the source data: ```sql SELECT from\_address, to\_address, CASE WHEN SAFE\_CAST(value AS NUMERIC) IS NULL THEN 0 ELSE SAFE\_C... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dataflow-bigquery-to-alloydb/README.md | main | gcp-professional-services | [
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# SRE Risk Analysis Tool This project is a tool designed to help SREs (Site Reliability Engineers) assess and manage risks associated with critical user journeys in their applications. It consists of a React frontend and a Flask backend that interacts with a Firestore database. ## Features \* \*\*Application Management... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/risk-analysis-asset/README.md | main | gcp-professional-services | [
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# SRE Risk Analysis Tool - Backend This Flask-based backend provides APIs to manage applications, critical user journeys (CUJs), and risks, storing data in a Firestore database. ## Features \* \*\*Application Management:\*\* \* Add new applications with names and descriptions. \* List all existing applications. \* \*\*... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/risk-analysis-asset/backend/README.md | main | gcp-professional-services | [
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# Sentiment Analysis with Kubeflow Pipelines, Cloud Dataflow, and Cloud Natural Language API Included code will build a Kubeflow Pipelines component and pipeline. The pipeline uses Cloud Dataflow to do sentiment analysis on New York Times headlines. Cloud DAtaflow uses Apache Beam (Java) to extract front page headlines... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/kubeflow-pipelines-sentiment-analysis/README.md | main | gcp-professional-services | [
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# gcs-hive-external-table-file-optimization Example solution to showcase impact of file count, file size, and file type on Hive external tables and query speeds ---- ## Table Of Contents 1. [About](#about) 2. [Use Case](#use-case) 3. [Architecture](#architecture) 4. [Guide](#guide) 5. [Sample Queries](#sample-queries) ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-hive-external-table-file-optimization/README.md | main | gcp-professional-services | [
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| | avro | DEFLATE | 1 | 18.4 | 9.20 | | avro | none | 1 | 44.7 | 15.59 | | json | none | 6851 | 0.01 | 476.52 | comments = 6851 x 10kb file(s)  comments\_json = 1 x 95.6mb file(s)  comments\_json\_gz = 1 x 17.1mb file(s) ![Stack-Resour... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gcs-hive-external-table-file-optimization/README.md | main | gcp-professional-services | [
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# IoT Nirvana This solution was built with the purpose of demonstrating an end-to-end Internet of Things architecture running on Google Cloud Platform. The purpose of the solution is to simulate the collection of temperature measures from sensors distributed all over the world and to follow temperature evolution by cit... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/iot-nirvana/README.md | main | gcp-professional-services | [
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pipeline's binary package will be stored \* \*\*[PUBSUB\_TOPIC]\*\* - the name of the PubSub topic created by the bootstrapping script, from which the Dataflow pipeline will read the temperature data; please note that this isn't the topic's canonical name, but instead the name relative to your project \* \*\*[BIGQUERY\... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/iot-nirvana/README.md | main | gcp-professional-services | [
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test the end to end solution, it is necessary first to start the temperature sensors simulation. Follow the steps below to achieve this: \* Go to the following address in your web browser, which will display the map of the Earth with 3 buttons at the bottom: \*\*Start\*\*, \*\*Update\*\*, \*\*Stop\*\* `https://[YOUR\_P... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/iot-nirvana/README.md | main | gcp-professional-services | [
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- [Reusable Plugins](#reusable-plugins-for-cloud-data-fusion-cdf--cdap) - [Overview](#overview) - [CheckPointReadAction, CheckPointUpdateAction](#checkpointreadaction-checkpointupdateaction) - [Dependencies](#dependencies) - [Setting up Firestore](#setting-up-firestore) - [Set Runtime Arguments](#set-runtime-arguments)... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-datafusion-functions-plugins/README.md | main | gcp-professional-services | [
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duplicate records are not in the destination table. `CheckPointReadAction` - reads checkpoints in Firestore DB and provides the data during runtime as environment variable `CheckPointUpdateAction` - updates checkpoints in Firestore DB (i.e., creates a new document and stores maximum update date / time from BQ so the ne... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-datafusion-functions-plugins/README.md | main | gcp-professional-services | [
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- Project ID: GCP project ID. - Dataset: Big Query dataset name. - Table Name: Big Query table name. \*\*Please see the following screenshot for example configuration:\*\*  # Putting it all together into a Pipeline `CheckPointReadAction` → `TruncateTableAction` → Database ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cloud-datafusion-functions-plugins/README.md | main | gcp-professional-services | [
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# Terraform for Deploying a KAS agent in a GKE cluster This repository provides Terraform code for deploying a KAS agent in a GKE cluster, to connect it with a Gitlab repository to automatically deploy, manage, and monitor your cloud-native solutions using GitOps practices. This creates resources in your cluster to dep... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gitlab-kas-gke/README.md | main | gcp-professional-services | [
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Kubernetes Service Account used by agent to manage deployments in product namespace - RBAC roles for KSA to read/write configMap of cluster - RBAC roles for KSA to read/write any k8s resources in product namespace ## Considerations - Setup remote backend in provider - Configure service account with appropriate permissi... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gitlab-kas-gke/README.md | main | gcp-professional-services | [
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## Requirements | Name | Version | |------|---------| | [gitlab](#requirement\\_gitlab) | >= 3.18.0 | | [helm](#requirement\\_helm) | >= 2.7.1 | | [kubernetes](#requirement\\_kubernetes) | >= 2.15.0 | ## Providers | Name | Version | |------|---------| | [gitlab](#provider\\_gitlab) | 15.8.0 | | [google](#provider\\_goo... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gitlab-kas-gke/terraform-docs.md | main | gcp-professional-services | [
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# Running a GKE application on spot nodes with on-demand nodes as fallback Spot VMs are unused virtual machines offered for a significant discount which makes them great for cost saving. However, spot VMs come with a catch: they can be shut down and taken away from your project at any time. Sometimes, they can even be ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gke-ha-setup-using-spot-vms/README.md | main | gcp-professional-services | [
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VMs have to be shut down, a new node has to be spun up in the on-demand pool, and the pods have to be restarted on the new node. Especially spinning up a new node takes some time, which means your application might experience temporary performance issues in this scenario. To avoid that, consider having sufficient insta... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/gke-ha-setup-using-spot-vms/README.md | main | gcp-professional-services | [
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# Redacting Sensitive Data Using the DLP API This example illustrates how to use the DLP api in a Cloud Function to redact sensitive data from log exports. The scrubbed logs will then be posted to a Pub/Sub topic to be ingested elsewhere. ## Getting Started These instructions will walk you through setting up your envir... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dlp/cloud_function_example/README.md | main | gcp-professional-services | [
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the identification and replacement of a small number of data types (EMAIL\_ADDRESS, CREDIT\_CARD\_NUMBER, and DATE\_OF\_BIRTH), however, the DLP API supports many more which can be found [here](https://cloud.google.com/dlp/docs/infotypes-reference) | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/dlp/cloud_function_example/README.md | main | gcp-professional-services | [
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# Contents - [Multi-Cluster ASM on Private Clusters](./infrastructure): Anthos Service Mesh (ASM) for multiple GKE clusters, using Terraform - [Twistlock PoC](./twistlock): Pod traffic security scanning, using ASM, Docker and Google Artifact Registry (GAR) - [Cloud SQL for PostgreSQL PoC](./postgres): Connecting GKE cl... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/README.md | main | gcp-professional-services | [
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creating clusters. For more information, see [Setting up clusters with Shared VPC](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-shared-vpc). In this sample, we create two private clusters in different subnets of the same VPC in the same project, and enable clusters to communicate to each other's API s... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/README.md | main | gcp-professional-services | [
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3. Run install\_asm\_mesh ``` install\_asm\_mesh ``` or, you can run the commands in install\_asm\_mesh step by step manually ``` # Navigate to your working directory. Binaries will be downloaded to this directory. cd ${WORK\_DIR} # Set up K8s config and context set\_up\_credential ${CLUSTER1\_CLUSTER\_NAME} ${CLUSTER1... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/README.md | main | gcp-professional-services | [
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# Twistlock Deployment Notes Because GCP ASM is built upon Istio service mesh for Kubernetes, Twistlock deployment on GCP ASM follows Twistlock's instruction of installation for Istio environment in general. \*\*NOTE:\*\* Installing Prisma Cloud (Twistlock) SaaS on Kubernetes requires the use of a bearer token. This ca... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/twistlock/README.md | main | gcp-professional-services | [
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is one daemon pod on each node of your cluster. ``` kubectl get pods -n twistlock ``` 2. Navigate back in Twistlock Cloud, and go to \_\_Compute\_\_ -> \_\_Radars\_\_ -> \_\_Hosts\_\_, you should see your cluster nodes are monitored there. 3. Still in Twistlock Cloud, go to \_\_Compute\_\_ -> \_\_Radars\_\_ -> \_\_Cont... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/twistlock/README.md | main | gcp-professional-services | [
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# Auto Encrypt PostgreSQL SSL Connection Using Istio Proxy Sidecar ### Summary PostgreSQL uses application-level protocol negotiation for SSL connection. Istio Proxy currently uses TCP-level protocol negotiation, so Istio Proxy sidecar errors out during SSL handshake when it tries to auto encrypt connection with Postgr... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/postgres/Istio-Sidecar.md | main | gcp-professional-services | [
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be prompted for password. You will see errors. #### Look into the Istio Proxy log You can read the logs in Cloud Logging. However, you may want to view sidecar log messages for the detailed network traffic information and errors with the following command. ``` kubectl logs deploy/postgres-istio -c istio-proxy -n ``` | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/postgres/Istio-Sidecar.md | main | gcp-professional-services | [
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# PostgreSQL Auto SSL Connection Using Cloud SQL Proxy ## Summary PostgreSQL uses application-level protocol negotiation for SSL connection. Istio Proxy currently uses TCP-level protocol negotiation, so Istio Proxy sidecar errors out during SSL handshake when it tries to auto encrypt connection with PostgreSQL. Please ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/postgres/README.md | main | gcp-professional-services | [
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serviceaccount \ ksa-sqlproxy \ iam.gke.io/gcp-service-account="sql-client@${PROJECT\_ID}.iam.gserviceaccount.com" \ -n YOUR\_NAMESPACE ``` 5. Deploy PostgreSQL client with Cloud SQL Proxy sidecar Take a look at the deployment YAML file, [postgres-cloudproxy.yaml](./postgres-cloudproxy.yaml). Please note the following ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/anthos-service-mesh-multicluster/postgres/README.md | main | gcp-professional-services | [
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# Cost Optimization Dashboard This repo contains SQL scripts for analyzing GCP Billing, Recommendations data and also a guide to setup the Cost Optimization dashboard. For sample dashboard [see here](https://datastudio.google.com/c/u/0/reporting/6cf564a4-9c94-4cfd-becd-b9c770ee7aa2/page/r34iB). ## Introduction The Cost... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/cost-optimization-dashboard/README.md | main | gcp-professional-services | [
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data analysis and aggregation #### Common Functions \* Compose a new query and copy the SQL at [common\_functions.sql](scripts/common\_functions.sql). \* Execute the query to create some required functions in the ```dashboard``` dataset. \* This is how the dataset will look like after the above step.  - [Create Cloud Function Doc](... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ml-claudeintegrations/BQ_RemoteFunction_Sample/README.md | main | gcp-professional-services | [
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# BQ+Claude Art Sample Project This README provides instructions for setting up and running the BQ+Claude3 Art Sample project. ## Prerequisites - Access to Google Cloud Platform (GCP) - BigQuery enabled in your GCP project ## Setup Instructions ### Step 1: Load Raw Data 1. Locate the `object.csv` file containing the ra... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-ml-claudeintegrations/Python_Notebook_Sample/README.md | main | gcp-professional-services | [
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# BigQuery Audit Log Anomany Detection BQ Audit log anomanly detection is a tool which uses [BigQuery Audit Logs](https://cloud.google.com/bigquery/docs/reference/auditlogs), specifically in the `AuditData` format, for automated analysis of Big Data Cloud environments with a focus on BigQuery. The tool summarized and a... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-auditlog-anomaly-detection/README.md | main | gcp-professional-services | [
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for outliers. For example, if you expect all emails in your bq environment to carrying about roughly equal jobs, you will set a lower sigma value. 3. If the individual score more than sigma from the standard deviation, the point will be flagged as an outlier. ### **Time Series Analysis** The algorithm to identify time ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-auditlog-anomaly-detection/README.md | main | gcp-professional-services | [
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# BQ Long Running Optimizer A utility that reads the entire SQL and provides a list of suggestions that would help to optimize the query and avoid the long running issues. ## Business Requirements One of the most frequently ocurring issues in BigQuery are Long Running Issues. This is seen in 2 cases :- 1. The Queries g... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-long-running-optimizer/Readme.md | main | gcp-professional-services | [
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## Use LIKE instead of REGEXP\_CONTAINS Recommendation In BigQuery, you can use the REGEXP\_CONTAINS function or the LIKE operator to compare strings. REGEXP\_CONTAINS provides more functionality, but also has a slower execution time. Using LIKE instead of REGEXP\_CONTAINS is faster, particularly if you don't need the ... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-long-running-optimizer/optimization/regex_contains/regex_contains.md | main | gcp-professional-services | [
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The Generic DDL Migration Utility does the following functionalities: 1. The script connects to Generic Database (Oracle, Snowflake, MSSQL, Vertica, Neteeza). 2. The script uses the metadata table (all\_tab\_columns) to retrieve the table schema information. 3. The script produces the "create table" statement using the... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-generic-ddl-migration-utility/README.md | main | gcp-professional-services | [
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The Generic BQ Converter Script does the following functionalities 1. The script reads the generic ddl files from the specified gcs path (output path of the generic\_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 path Be... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-generic-ddl-migration-utility/DDL_Converter/generic_bq_converter.md | main | gcp-professional-services | [
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Below packages are need to run the script: google-cloud-secret-manager google-cloud-bigquery google-cloud-storage google-api-core sudo apt install unixodbc Steps to run this script: 1. Create the generic-ddl-extraction-config.json file and place it in the gcs bucket. 2. Create the object\_name\_mapping.json file and pl... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-generic-ddl-migration-utility/DDL_Extractor/generic_ddl_extraction.md | main | gcp-professional-services | [
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The BQ Table Creator Script does the following functionalities 1. Reads the output sql file created by the mssql/netezza/vertica bq converter script 2. The script create the Bigquery Tables in the specified target dataset. 3. The table structure will include source columns, metadata columns and paritioning and clusteri... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-generic-ddl-migration-utility/BQ_Table_Creator/generic_bq_table_creator.md | main | gcp-professional-services | [
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The Archive DDL Script archive the DDL files created by the scripts (generic\_ddl\_extraction.py, generic\_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-requsitie o... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/bigquery-generic-ddl-migration-utility/DDL_Archiver/generic_archive_ddl.md | main | gcp-professional-services | [
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# Rotate service account keys in secret manager The program helps to rotate the service account key in secret manager. - \*\*Step 1:\*\* Creata a Pub/Sub Topic. Secret manager will publish a message to this topic as the secret approaches the expiration time. ``` gcloud pubsub topics create "projects/PUBSUB\_PROJECT\_ID... | https://github.com/GoogleCloudPlatform/professional-services/blob/main/examples/rotate-service-account-keys/Readme.md | main | gcp-professional-services | [
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