Install & Compatibility
Where this runs
tested against v1.12.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 36.9s · import 0.000s · 665MB
681MB installed
● package 681MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
dbt-bigquery adapter is used via dbt CLI, not direct Python imports
✓ dbt run
dbt-bigquery is an adapter plugin for dbt Core. Users primarily interact with it through the dbt Command Line Interface (CLI) and YAML configuration files (profiles.yml, dbt_project.yml), rather than direct Python import statements in their models or scripts.
This quickstart demonstrates how to set up a dbt project to connect to Google BigQuery using a service account. It involves creating a `profiles.yml` file, initializing a dbt project, creating a simple SQL model, and then running dbt commands. Ensure you have a Google Cloud Project with BigQuery enabled and a service account key file.
# Create a profiles.yml file in ~/.dbt/ or your dbt project directory.
# Ensure GCP_PROJECT_ID and GOOGLE_APPLICATION_CREDENTIALS (path to service account JSON) are set as environment variables.
# ~/.dbt/profiles.yml
# ---
# my_bigquery_project:
# target: dev
# outputs:
# dev:
# type: bigquery
# method: service-account
# project: "{{ env_var('GCP_PROJECT_ID', 'your-gcp-project-id') }}"
# dataset: "dbt_dev_dataset" # This dataset must exist in BigQuery
# keyfile: "{{ env_var('GOOGLE_APPLICATION_CREDENTIALS', 'path/to/your/service-account.json') }}"
# threads: 4
# location: US # Or your BigQuery dataset location (e.g., EU, asia-northeast1)
# job_execution_timeout_seconds: 300
# ---
# 1. Initialize a new dbt project (if you don't have one):
# dbt init my_new_dbt_project
# cd my_new_dbt_project
# 2. Edit dbt_project.yml to reference your profile (e.g., 'my_bigquery_project'):
# name: 'my_new_dbt_project'
# version: '1.0.0'
# config-version: 2
# profile: 'my_bigquery_project' # Must match a profile name in profiles.yml
# 3. Create a sample SQL model: models/my_first_model.sql
# ---
# -- models/my_first_model.sql
# SELECT
# current_timestamp() as current_time,
# 'Hello dbt-bigquery!' as message
# ---
# 4. Set environment variables (replace with your actual values):
# export GCP_PROJECT_ID="your-gcp-project-id"
# export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account.json"
# 5. Run dbt commands:
# dbt debug # To verify your connection to BigQuery
# dbt run # To execute the model and create a view/table in BigQuery
dbt --version
Upgrade
Version history
1.12.0latest on PyPI · released Jul 16, 2026
Audit
Dependencies
dbt-corerequireddbt-bigquery is an adapter for dbt Core and requires it to function.
Google Cloud SDKrequiredRequired for authentication (gcloud auth) and interacting with Google Cloud services like BigQuery and Cloud Storage.
BigQuery API, Cloud Storage API, Compute Engine API, Dataform API, Identity and Access Management API, Vertex AI API (GCP)requiredThese APIs must be enabled in your Google Cloud Project for full dbt-bigquery functionality, especially for Python models and BigQuery DataFrames.