dbt-bigquery

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

dbt-bigquery is a dbt adapter that enables data transformation workflows in Google BigQuery, allowing analysts and engineers to organize, cleanse, and prepare raw warehouse data using dbt's SQL and YAML-based practices.

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

pip install dbt-bigquery

uv add dbt-bigquery

poetry add dbt-bigquery

Installing dbt-bigquery

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.

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 run

Requires Python >=3.10.0 and valid Google Cloud authentication (OAuth, service account key, or Application Default Credentials).

Verify 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

Evidence: dbt_bigquery-1.12.0-py3-none-any.whl

Keywords: adapter, adapters, bigquery, database, dbt, dbt Cloud, dbt Core, dbt Labs, dbt-core, elt, google

Tags

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