cloud-foundation-fabric/examples/data-solutions

GCP Data Services examples

The examples 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.

Examples

GCE and GCS CMEK via centralized Cloud KMS

This example implements CMEK for GCS and GCE, via keys hosted in KMS running in a centralized project. The example 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 Storage to Bigquery with Cloud Dataflow with least privileges

This example 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.

Data Platform Foundations

This example implements a robust and flexible Data Foundation on GCP that provides opinionated defaults, allowing customers to build and scale out additional data pipelines quickly and reliably.

SQL Server Always On Availability Groups

This example 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.

Cloud SQL instance with multi-region read replicas

This example creates a Cloud SQL instance with multi-region read replicas as described in the Cloud SQL for PostgreSQL disaster recovery article.

Data Playground starter with Cloud Vertex AI Notebook and GCS

This example 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.