dbt-bigquery
The BigQuery adapter plugin for dbt
Decision gist · record as of 2026-08-14
Yes. dbt-bigquery is production-stable, actively maintained, and widely adopted (top 5000 PyPI packages). It has low install friction, no security vulnerabilities, and a permissive license. Install it if you use BigQuery and want to adopt dbt's SQL-based transformation practices; skip it if you prefer procedural data pipelines or are not yet using BigQuery.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.0 and valid Google Cloud authentication (OAuth, service account key, or Application Default Credentials).
- Low install friction; wheel-only distribution.
- Active maintenance with recent release (29 days ago) and ongoing commits.
License · maintenance · safety
permissive license (permissive) — Permissive license (Apache Software License) means you can use, modify, and distribute this package with minimal legal constraints in most contexts.
last release 2026-07-16 (29 days) · last repo commit 2026-08-14 · 231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,989,402 downloads/mo, #1,995 on PyPI
Alternatives
Verify before relying
pip install dbt-bigquery
In your dbt profiles.yml:
my-bigquery-db:
target: dev
outputs:
dev:
type: bigquery
project-id: my-project
dataset-id: my_dataset
method: oauth
Then run: dbt run- Whether the package includes built-in support for specific BigQuery features (clustering, partitioning, materialized views) beyond standard dbt capabilities.
- Performance characteristics and scalability limits when working with very large BigQuery datasets.
- Whether dbt-bigquery handles all BigQuery-specific SQL dialects or requires query translation.
What it is and what it does
dbt-bigquery is a plugin that connects dbt (a data transformation framework) to Google BigQuery, enabling teams to build and maintain data pipelines using SQL and YAML configuration rather than procedural code. It sits in the 'T' (transform) layer of ELT workflows, taking raw data already loaded into BigQuery and reshaping it for analysis through version-controlled, testable transformation logic.
The package bundles dbt-core with BigQuery-specific connection handling, authentication via Google Cloud libraries (google-auth, google-cloud-bigquery, and others), and integration with BigQuery's compute and storage. It supports Python 3.10, 3.11, 3.12, 3.13 and is actively maintained by dbt Labs, with a permissive Apache license and no known security vulnerabilities.
Use it for
- Build repeatable SQL transformation pipelines in BigQuery with version control and testing, replacing manual SQL scripts.
- Organize raw warehouse data into clean, denormalized tables ready for BI tools and analytics.
- Collaborate on data models across teams using dbt's YAML documentation and lineage tracking.
- Automate incremental data loads and refresh schedules in BigQuery without writing custom orchestration.
- Integrate BigQuery transformations into dbt Cloud for scheduling, monitoring, and CI/CD workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dbt-bigquery is production-stable, actively maintained, and widely adopted (top 5000 PyPI packages). It has low install friction, no security vulnerabilities, and a permissive license. Install it if you use BigQuery and want to adopt dbt's SQL-based transformation practices; skip it if you prefer procedural data pipelines or are not yet using BigQuery.
Install
dbt-bigquery on PyPI
Before you install
Low install friction; wheel-only distribution. Active maintenance with recent release (29 days ago) and ongoing commits. Supports Python 3.10, 3.11, 3.12, 3.13. Depends on dbt-core and Google Cloud libraries, all widely maintained.
Requires Python >=3.10.0 and valid Google Cloud authentication (OAuth, service account key, or Application Default Credentials).
License in practice
Permissive license (Apache Software License) means you can use, modify, and distribute this package with minimal legal constraints in most contexts.
Quickstart
pip install dbt-bigquery
In your dbt profiles.yml:
my-bigquery-db:
target: dev
outputs:
dev:
type: bigquery
project-id: my-project
dataset-id: my_dataset
method: oauth
Then run: dbt runVerify before relying
- Whether the package includes built-in support for specific BigQuery features (clustering, partitioning, materialized views) beyond standard dbt capabilities.
- Performance characteristics and scalability limits when working with very large BigQuery datasets.
- Whether dbt-bigquery handles all BigQuery-specific SQL dialects or requires query translation.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesdbt-adaptersdbt-commondbt-coregoogle-api-coregoogle-authgoogle-cloud-aiplatformgoogle-cloud-bigquerygoogle-cloud-dataprocgoogle-cloud-storagenbformat |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,989,402 / month, #1,995 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/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: dbt_bigquery-1.12.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “dbt bigquery adapter”
- dbt-bigquerydbt-bigquery is a dbt adapter that enables data transformation…
- dbt-adaptersProvides base adapter protocols and shared functionality that…
- dbt-commonProvides shared utilities and common code used by dbt-core and dbt…
Give your agent the search over MCP, or paste the wish link into any chat.
More Database packages
psycopg2-binary is a PostgreSQL database adapter for Python that implements the DB API 2.0 specification, enabling Python applications to connect to and query PostgreSQL databases with thread-safe concurrent operations.
Python client library for connecting to and executing commands against Redis key-value stores, supporting both synchronous and asynchronous operations.
Install it if your application needs to interact with Redis; the only prerequisite is a running Redis server instance.
YDB Python SDK is the official client library for connecting to and querying YDB databases from Python applications.
Install it if you need to connect Python applications to YDB databases.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
sqlparse tokenizes SQL text into a tree of statements, clauses, and expressions, and provides functions to split scripts, format queries, and inspect parsed tokens without validating dialect or syntax.
Install it if you need to manipulate, format, or analyze SQL text programmatically.
Provides base adapter protocols and shared functionality that database adapters use to integrate with dbt-core, handling connections, dialect translation, relation caching, and core interface management.
See also dbt-core · dbt-dremio · dbt-glue · dbt-osmosis · dbt-postgres · dbt-redshift · dbt-snowflake · dbt-spark · dbt-trino · dbt-fabricspark