sqlalchemy-bigquery
SQLAlchemy dialect for BigQuery
Decision gist · record as of 2026-08-14
Yes. This is a production-stable, actively maintained dialect with no known vulnerabilities. Install it if you need to access BigQuery through SQLAlchemy's ORM or query interface. The only prerequisite is setting up Google Cloud authentication and enabling the BigQuery Storage API—both are one-time setup steps. Low install friction and permissive licensing make it a straightforward choice for BigQuery integration.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10.
- Requires Google Cloud project setup, BigQuery Storage API enabled, and authentication credentials (service account JSON or environment-based).
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute the package freely in commercial and private projects without restriction.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 5,371 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 79,820,407 downloads/mo, #425 on PyPI
Alternatives
Verify before relying
pip install sqlalchemy-bigquery
from sqlalchemy import create_engine, Table, MetaData, select, func
engine = create_engine('bigquery://project')
table = Table('dataset.table', MetaData(bind=engine), autoload=True)
result = engine.execute(select([func.count('*')], from_obj=table)).scalar()- Whether bqstorage extras installation significantly improves performance for your typical dataset sizes.
- Specific BigQuery features or SQL syntax that may not be fully supported by the dialect.
What it is and what it does
SQLAlchemy BigQuery is a SQLAlchemy dialect that bridges Python's SQLAlchemy ORM and query builder to Google Cloud BigQuery. It lets you write database queries using SQLAlchemy's familiar Python API instead of raw SQL, and execute them against BigQuery tables. The package handles authentication, connection pooling, and translation of SQLAlchemy expressions into BigQuery-compatible queries.
You install it alongside sqlalchemy and the google-cloud-bigquery client library. Once configured with credentials and a project ID, you create a SQLAlchemy engine pointing to BigQuery and use it like any other database—defining tables, building queries with select() and filters, and fetching results. It supports specifying datasets, locations, batch sizes, and query job configuration through connection string parameters.
Use it for
- Build data pipelines that read from BigQuery using SQLAlchemy ORM instead of raw SQL.
- Migrate an application from a traditional SQL database to BigQuery without rewriting query code.
- Use SQLAlchemy's declarative table definitions and relationship mapping with BigQuery tables.
- Integrate BigQuery into Flask-SQLAlchemy or other frameworks that expect a SQLAlchemy dialect.
- Query BigQuery datasets from Python applications that already depend on SQLAlchemy.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is a production-stable, actively maintained dialect with no known vulnerabilities. Install it if you need to access BigQuery through SQLAlchemy's ORM or query interface. The only prerequisite is setting up Google Cloud authentication and enabling the BigQuery Storage API—both are one-time setup steps. Low install friction and permissive licensing make it a straightforward choice for BigQuery integration.
Install
sqlalchemy-bigquery on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance—released 8 days ago with 5371 repository stars. Requires Python >= 3.10 and depends on google-cloud-bigquery, google-auth, and sqlalchemy, all well-maintained Google Cloud libraries.
Requires Python >= 3.10. Requires Google Cloud project setup, BigQuery Storage API enabled, and authentication credentials (service account JSON or environment-based).
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute the package freely in commercial and private projects without restriction.
Quickstart
pip install sqlalchemy-bigquery
from sqlalchemy import create_engine, Table, MetaData, select, func
engine = create_engine('bigquery://project')
table = Table('dataset.table', MetaData(bind=engine), autoload=True)
result = engine.execute(select([func.count('*')], from_obj=table)).scalar()Verify before relying
- Whether bqstorage extras installation significantly improves performance for your typical dataset sizes.
- Specific BigQuery features or SQL syntax that may not be fully supported by the dialect.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesgoogle-api-coregoogle-authgoogle-cloud-bigquerypackagingsqlalchemy |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 79,820,407 / month, #425 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 :: MIT 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 :: Database :: Front-Ends |
Evidence: sqlalchemy_bigquery-1.17.2-py3-none-any.whl
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