google-cloud-bigquery-biglake

What it is and what it does

This is Google's official Python client for BigLake Metastore, a managed metadata service that lets you query Apache Iceberg tables stored in BigQuery. It wraps the BigLake API to provide Python developers with a standard interface for metastore operations—creating, listing, and managing Iceberg table metadata without managing infrastructure. The library depends on google-api-core, google-auth, grpcio, proto-plus, and protobuf to handle authentication, gRPC transport, and protocol buffer serialization.

You use it when you need programmatic access to BigLake metadata from Python—typically to integrate Iceberg table discovery or management into data pipelines, automation scripts, or applications that work with open-source data formats in BigQuery. It's in Beta status and actively maintained, supporting modern Python versions (3.10 and later). Setup requires a Google Cloud project with BigLake enabled and proper authentication configured.

Use it for:

• Discover and list Iceberg tables available in BigLake Metastore from a Python application.
• Automate metadata operations on Iceberg tables as part of a data pipeline or ETL workflow.
• Integrate BigLake table access into a data catalog or governance tool running on Google Cloud.
• Query Iceberg table schema and partition information programmatically for data validation.
• Build custom applications that manage open-source table formats alongside BigQuery datasets.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Python client library for accessing BigLake Metastore, enabling queries against Apache Iceberg tables in BigQuery through a serverless, managed metastore API.

Yes, if you are working with Apache Iceberg tables in BigQuery and need Python-based programmatic access to BigLake Metastore. The library is actively maintained, has low install friction, carries a permissive license, and is backed by Google's infrastructure. No known vulnerabilities. Install it only if your project requires BigLake API integration; it is not a general-purpose tool.

Install

google-cloud-bigquery-biglake on PyPI

pip install google-cloud-bigquery-biglake

uv add google-cloud-bigquery-biglake

poetry add google-cloud-bigquery-biglake

Installing google-cloud-bigquery-biglake

Before you install

Low friction installation via pip with standard Google Cloud dependencies. Active maintenance with recent releases; repository has 5373 stars and last commit on 2026-08-14. Supports current Python versions (3.10 through 3.14).

License in practice

Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions—standard for Google Cloud client libraries.

Quickstart

pip install google-cloud-bigquery-biglake

from google.cloud import biglake

client = biglake.MetastoreServiceClient()
# Requires Google Cloud authentication and BigLake API enabled

Requires Python >= 3.10; Google Cloud project with BigLake API enabled and valid authentication credentials (via google-auth).

Verify before relying

• Specific BigLake API methods and their signatures beyond the client instantiation pattern shown in the description.
• Whether the library handles connection pooling, retry logic, or rate limiting automatically.
• Performance characteristics when querying large Iceberg table metadata or executing bulk operations.

Package facts

Evidence: google_cloud_bigquery_biglake-0.8.0-py3-none-any.whl

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