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* factories refactor doc * Adds file schema and filesystem organization * Update 20231106-factories.md * move factories out of blueprints and create new factories README * align factory in billing-account module * align factory in dataplex-datascan module * align factory in billing-account module * align factory in net-firewall-policy module * align factory in dns-response-policy module * align factory in net-vpc-firewall module * align factory in net-vpc module * align factory variable names in FAST * remove decentralized firewall blueprint * bump terraform version * bump module versions * update top-level READMEs * move project factory to modules * fix variable names and tests * tfdoc * remove changelog link * add project factory to top-level README * fix cludrun eventarc diff * fix README * fix cludrun eventarc diff --------- Co-authored-by: Simone Ruffilli <sruffilli@google.com> |
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bq-ml | ||
cloudsql-multiregion | ||
cmek-via-centralized-kms | ||
composer-2 | ||
data-platform-foundations | ||
data-platform-minimal | ||
data-playground | ||
gcs-to-bq-with-least-privileges | ||
shielded-folder | ||
sqlserver-alwayson | ||
vertex-mlops | ||
README.md |
README.md
GCP Data Services blueprints
The blueprints in this folder implement typical data service topologies and end-to-end scenarios, that allow testing specific features like Cloud KMS to encrypt your data, or VPC-SC to mitigate data exfiltration.
They are meant to be used as minimal but complete starting points to create actual infrastructure, and as playgrounds to experiment with specific Google Cloud features.
Blueprints
Cloud SQL instance with multi-region read replicas
This blueprint creates a Cloud SQL instance with multi-region read replicas as described in the Cloud SQL for PostgreSQL disaster recovery article.
GCE and GCS CMEK via centralized Cloud KMS
This blueprint implements CMEK for GCS and GCE, via keys hosted in KMS running in a centralized project. The blueprint shows the basic resources and permissions for the typical use case of application projects implementing encryption at rest via a centrally managed KMS service.
Cloud Composer version 2 private instance, supporting Shared VPC and external CMEK key
This blueprint creates a Cloud Composer version 2 instance on a VPC with a dedicated service account. The solution supports as inputs: a Shared VPC and Cloud KMS CMEK keys.
Data Platform Foundations
This blueprint implements a robust and flexible Data Platform on GCP that provides opinionated defaults, allowing customers to build and scale out additional data pipelines quickly and reliably.
Minimal Data Platform
This blueprint implements a minimal Data Platform on GCP that provides opinionated defaults, allowing customers to build and scale out additional data pipelines quickly and reliably.
Data Playground starter with Cloud Vertex AI Notebook and GCS
This blueprint creates a Vertex AI Notebook running on a VPC with a private IP and a dedicated Service Account. A GCS bucket and a BigQuery dataset are created to store inputs and outputs of data experiments.
Cloud Storage to Bigquery with Cloud Dataflow with least privileges
This blueprint implements resources required to run GCS to BigQuery Dataflow pipelines. The solution rely on a set of Services account created with the least privileges principle.
SQL Server Always On Availability Groups
This blueprint implements SQL Server Always On Availability Groups using Fabric modules. It builds a two node cluster with a fileshare witness instance in an existing VPC and adds the necessary firewalling. The actual setup process (apart from Active Directory operations) has been scripted, so that least amount of manual works needs to performed.
MLOps with Vertex AI
This blueprint implements the infrastructure required to have a fully functional MLOPs environment using Vertex AI: required GCP services activation, Vertex Workbench, GCS buckets to host Vertex AI and Cloud Build artifacts, Artifact Registry docker repository to host custom images, required service accounts, networking and Workload Identity Federation Provider for Github integration (optional).
Shielded Folder
This blueprint implements an opinionated folder configuration according to GCP best practices. Configurations implemented on the folder would be beneficial to host workloads inheriting constraints from the folder they belong to.
BigQuery ML and Vertex AI Pipeline
This blueprint provides the necessary infrastructure to create a complete development environment for building and deploying machine learning models using BigQuery ML and Vertex AI. With this blueprint, you can deploy your models to a Vertex AI endpoint or use them within BigQuery ML.