1. Unified Data Governance for AI Projects on GCP Dataplex


    In order to set up unified data governance for AI projects on Google Cloud Platform (GCP) using Dataplex, we will utilize Pulumi to define and deploy the necessary infrastructure components. Here, we will create a Lake, Zone, and Asset within Dataplex to organize, manage, and govern data across your data lakes and warehouses in a centralized manner. This allows AI projects to have well-governed and discoverable data.

    • A Lake in Dataplex is a centralized metadata repository within a specific project and location which acts as a container for all the data that is going to be managed and governed. The Lake resource encapsulates this entity.
    • Within a Lake, you can organize data into logical Zones. A Zone represents a logical zone within the lake such as a raw zone, curated zone, or business zone. Each Zone supports a defined purpose or usage pattern.
    • An Asset within a Zone represents a link to data stored in GCP's data storage services, such as BigQuery datasets or Cloud Storage buckets. The creation of an Asset allows you to define how the data will be handled within the Zone.

    The following Pulumi program in Python will set up these components for you. Note that we assume that Pulumi's GCP plugin and authentication with GCP is already configured on the machine where the deployment will be executed.

    import pulumi import pulumi_gcp as gcp # The name of your GCP project and the location where you want to host your data governance infrastructure. project_name = "your-gcp-project" lake_location = "us-central1" # Create a Dataplex Lake to serve as a centralized repository for your data. lake = gcp.dataplex.Lake("my-lake", name="my-lake", project=project_name, location=lake_location, description="Primary Lake for data governance") # Within the Lake, create a Dataplex Zone for better data organization. # For example, a raw zone for ingesting raw data. zone = gcp.dataplex.Zone("my-zone", lake=lake.name, name="raw-zone", type="RAW", project=project_name, location=lake_location, resource_spec=gcp.dataplex.ZoneResourceSpecArgs( location_type="SINGLE_REGION", ), discovery_spec=gcp.dataplex.ZoneDiscoverySpecArgs( enabled=True, csv_options=gcp.dataplex.ZoneDiscoverySpecCsvOptionsArgs( encoding="UTF-8", delimiter=",", header_rows=1 ) )) # Create a Dataplex Asset which is a link to an actual storage bucket where the data resides. asset = gcp.dataplex.Asset("my-asset", lake=lake.name, zone=zone.name, name="raw-data-asset", project=project_name, location=lake_location, dataplex_zone=zone.name, resource_spec=gcp.dataplex.AssetResourceSpecArgs( type="STORAGE_BUCKET", name="gs://my-raw-data-bucket" # Replace with the name of your GCP Storage Bucket ), discovery_spec=gcp.dataplex.AssetDiscoverySpecArgs( enabled=True, schedule="0 2 * * *")) # Daily discovery job at 2 AM. # Export the URLs of the Dataplex Lake, Zone, and Asset for easy access. pulumi.export("lake_id", lake.id) pulumi.export("zone_id", zone.id) pulumi.export("asset_id", asset.id)

    Here's what the Pulumi program constructs:

    • gcp.dataplex.Lake: Creation of a Dataplex Lake called my-lake. It will act as the central metadata repository for your data. You must replace your-gcp-project with your actual GCP project ID and choose an appropriate location.

    • gcp.dataplex.Zone: Establishment of a zone within the lake. This example uses a raw zone type, which is typically where you place ingested data in its original, unprocessed form.

    • gcp.dataplex.Asset: An asset within the zone to connect to a specific Cloud Storage bucket (referenced by the bucket's URI in gs://my-raw-data-bucket). The asset configures a discovery job to run daily at 2 AM.

    By running this program with Pulumi, the specified resources will be created and configured in your GCP project, providing a foundation for unified data governance suitable for AI projects.