Google Cloud Dataplex

2.17.0 · active · verified Sun Mar 29

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.

Warnings

breaking Some metadata stored in Dataplex Universal Catalog changed on January 12, 2026, to align with original source systems (e.g., Vertex AI, Bigtable, Spanner). Workloads that depend on the specific structure or content of this metadata will need to be adjusted to preserve continuity.
Fix: Review release notes and documentation for specific metadata changes and update your code to reflect the new structure or consistency with source systems.
gotcha Dataplex enforces strict location constraints for resources. Zones (regional or multi-regional) and their associated assets (e.g., GCS buckets, BigQuery datasets) must strictly match the zone's location type. Attempting to add an asset that violates these constraints (e.g., a 'EU' multi-region BigQuery dataset to a 'europe-west1' regional zone) will result in asset attachment failures.
Fix: 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.
deprecated Dataplex Explore was deprecated on July 22, 2024. Functionality provided by Dataplex Explore is now expected to be handled by BigQuery Studio.
Fix: Migrate any existing Dataplex Explore workloads or functionalities to BigQuery Studio as per the official migration instructions.
gotcha When programmatically querying Dataplex Catalog Entries using the Python client, you might only retrieve custom Aspect *names* but not their corresponding *values* by default.
Fix: 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`.

Install

pip install google-cloud-dataplex Install stable version

Imports

DataplexServiceClient
```
from google.cloud import dataplex_v1
```
The v1 suffix indicates the API version. Always import the versioned client.

Quickstart

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.

import os
from google.cloud import dataplex_v1

def list_lakes(project_id: str, location: str):
    """Lists Dataplex lakes in a given project and location."""
    try:
        client = dataplex_v1.DataplexServiceClient()
        parent = f"projects/{project_id}/locations/{location}"

        print(f"Listing lakes in {parent}:")
        # API calls often return an iterable (pager) for list methods
        for lake in client.list_lakes(parent=parent):
            print(f"- {lake.name} (State: {lake.state.name})")
        print("Lakes listed successfully.")
    except Exception as e:
        print(f"An error occurred: {e}")
        print("Ensure 'gcloud auth application-default login' has been run or GOOGLE_APPLICATION_CREDENTIALS is set.")
        print("Also, verify that the Dataplex API is enabled for your project and the service account has necessary permissions.")

if __name__ == "__main__":
    PROJECT_ID = os.environ.get("GOOGLE_CLOUD_PROJECT", "your-gcp-project-id")
    LOCATION = "us-central1" # Or your desired region, e.g., "global" for some resources

    if PROJECT_ID == "your-gcp-project-id":
        print("Please set the 'GOOGLE_CLOUD_PROJECT' environment variable or replace 'your-gcp-project-id' with your actual GCP project ID.")
    else:
        list_lakes(PROJECT_ID, LOCATION)

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