google-cloud-bigquery-storage
Google Cloud Bigquery Storage API client library
Decision gist · record as of 2026-08-14
Yes. This is the official, actively maintained client for BigQuery Storage backed by Google Cloud infrastructure. It has no known vulnerabilities, low install friction, permissive licensing, and is widely used (top 1000 PyPI packages). Install it if you need to read or write data at scale in BigQuery; skip it if you only use the standard BigQuery API for small queries or one-off analytics.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Google Cloud Platform project setup, BigQuery Storage API enabled, and authentication credentials configured (via GOOGLE_APPLICATION_CREDENTIALS or Application Default Credentials).
- Low friction installation with a pure Python wheel.
- Actively maintained as of 8 days ago with a large repository footprint (5371 stars).
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute this library freely provided you retain the license notice.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 5,371 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 40,553,934 downloads/mo, #680 on PyPI
Alternatives
Verify before relying
pip install google-cloud-bigquery-storage
from google.cloud import bigquery_storage
client = bigquery_storage.BigQueryReadClient()
# Use client to read from BigQuery Storage- Whether the package supports reading from external data sources or only native BigQuery tables.
- Performance characteristics and throughput limits compared to direct BigQuery API.
- Whether streaming ingestion supports exactly-once delivery semantics or at-least-once.
What it is and what it does
This is the official Python client for Google BigQuery Storage, a managed service for high-throughput, low-latency data access to BigQuery. It wraps the BigQuery Storage API to enable bulk reads and streaming writes, complementing the standard BigQuery client library for workloads that prioritize throughput over latency or require columnar data export.
The library handles authentication via Google Cloud credentials, manages gRPC connections to the BigQuery Storage service, and provides a Pythonic interface to read table snapshots, write streaming records, and manage read sessions. It depends on google-api-core for common Google Cloud patterns, google-auth for credential handling, grpcio for transport, and protobuf for message serialization. Installation is straightforward and requires only Python 3.10 or later.
Use it for
- Export large BigQuery tables to local or cloud storage for batch processing, machine learning pipelines, or data warehousing.
- Stream application events or sensor data into BigQuery with low latency for real-time analytics and dashboards.
- Build ETL pipelines that read from BigQuery Storage and transform data using pandas, Spark, or other Python frameworks.
- Integrate BigQuery as a data source for Python-based analytics, reporting, or business intelligence tools.
- Perform high-throughput data replication or migration between BigQuery projects or external systems.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is the official, actively maintained client for BigQuery Storage backed by Google Cloud infrastructure. It has no known vulnerabilities, low install friction, permissive licensing, and is widely used (top 1000 PyPI packages). Install it if you need to read or write data at scale in BigQuery; skip it if you only use the standard BigQuery API for small queries or one-off analytics.
Install
google-cloud-bigquery-storage on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained as of 8 days ago with a large repository footprint (5371 stars). Requires modern Python (3.10+) and depends on standard Google Cloud libraries (google-api-core, google-auth, grpcio, proto-plus, protobuf).
Requires Google Cloud Platform project setup, BigQuery Storage API enabled, and authentication credentials configured (via GOOGLE_APPLICATION_CREDENTIALS or Application Default Credentials).
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute this library freely provided you retain the license notice.
Quickstart
pip install google-cloud-bigquery-storage
from google.cloud import bigquery_storage
client = bigquery_storage.BigQueryReadClient()
# Use client to read from BigQuery StorageVerify before relying
- Whether the package supports reading from external data sources or only native BigQuery tables.
- Performance characteristics and throughput limits compared to direct BigQuery API.
- Whether streaming ingestion supports exactly-once delivery semantics or at-least-once.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesgoogle-api-coregoogle-authgrpcioproto-plusprotobuf |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 40,553,934 / month, #680 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Internet |
Evidence: google_cloud_bigquery_storage-2.40.0-py3-none-any.whl
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See also google-cloud-bigquery-biglake · google-cloud-bigquery-connection · google-cloud-bigquery-datatransfer · google-cloud-bigquery-logging · google-cloud-bigtable · google-cloud-dataform · google-cloud-vectorsearch · google-cloud-alloydb · google-cloud-bigquery-reservation · bigquery