Google Cloud Dataplex is a unified data governance platform that provides an intelligent data fabric to centrally manage, monitor, and govern data across data lakes, data warehouses, and data marts. It enables consistent controls, trusted data access, and powers analytics at scale. The Python client library is currently at version 2.17.0 and is actively maintained with frequent releases.
pip install google-cloud-dataplexVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to instantiate the Dataplex client and list existing lakes within a specified Google Cloud project and location. Ensure your Google Cloud project ID and an appropriate location are set.
Review release notes and documentation for specific metadata changes and update your code to reflect the new structure or consistency with source systems.
Ensure that the location of your Dataplex zones and the underlying data assets (GCS buckets, BigQuery datasets) are compatible and correctly aligned according to Dataplex's strict location hierarchy rules.
Migrate any existing Dataplex Explore workloads or functionalities to BigQuery Studio as per the official migration instructions.
To retrieve the full Aspect values, ensure you set the `view` parameter (e.g., `EntryView.FULL` or `EntryView.ALL`) when calling methods like `CatalogServiceClient.get_entry` or `CatalogServiceClient.list_entries`.
Upgrade your Python environment to version 3.10 or higher, and then ensure `google-cloud-dataplex_v1` and its dependencies (e.g., `google-api-core`, `google-auth`) are updated to their latest compatible versions.
Ensure the 'GOOGLE_CLOUD_PROJECT' environment variable is set, or provide the project ID explicitly when initializing Dataplex client objects (e.g., `client = dataplex.DataplexServiceClient(project='your-gcp-project-id')`).
Grant the appropriate IAM roles (e.g., `roles/dataplex.admin`, `roles/dataplex.viewer`, `roles/dataplex.editor`, or specific granular permissions like `dataplex.metadataFeeds.create`, `bigquery.dataOwner`) to the service account or user on the relevant Google Cloud project, lake, zone, or resource via the Google Cloud Console or `gcloud` CLI.
Enable the required API(s) through the Google Cloud Console by navigating to 'APIs & Services' > 'Library', searching for the specific API (e.g., 'Dataplex API'), and clicking 'Enable'. Allow a few minutes for the API to fully activate before retrying.
Either manually delete all nested resources (zones, assets) within the lake before attempting to delete the lake, or, if using the Python client library, set the `force=True` parameter in the `DeleteLakeRequest` to perform a cascading deletion.
Ensure the `DataplexServiceClient` is initialized with the correct region where your Dataplex resources are located. You can specify the endpoint by setting `client_options={'api_endpoint': 'dataplex.<REGION>.googleapis.com'}` during client initialization. Also, verify the project ID is correct and the service account has permissions in that specific region.No dependency data recorded yet.