airflow-dbt

What it is and what it does

airflow-dbt is a collection of Airflow operators that wrap dbt command-line operations, allowing you to orchestrate dbt workflows (seed, snapshot, run, test, docs generate, deps) as tasks in Airflow DAGs. Each operator accepts arguments like profiles_dir, target, models, exclude, select, and vars to pass through to the underlying dbt command, and can be chained together to form a complete data transformation pipeline.

The package depends only on apache-airflow and assumes dbt CLI is available on the system PATH or can be specified via the dbt_bin argument. It is designed to integrate with Airflow's task scheduling and retry logic, and includes a hook for combining dbt commands with other tasks in the same operator.

Use it for:

• Schedule dbt seed, snapshot, run, and test operations as daily or hourly Airflow tasks within a larger data pipeline
• Orchestrate dbt model selection and exclusion logic through Airflow parameters without modifying dbt code
• Chain dbt docs generation with downstream tasks that upload documentation to a web server or artifact store
• Integrate dbt transformations into multi-step Airflow DAGs alongside data ingestion, validation, and reporting tasks

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Provides Airflow operators that wrap dbt CLI commands (seed, snapshot, run, test, docs generate, deps) to orchestrate dbt workflows within Airflow DAGs.

No. The package is abandoned (last release 2021-09-03, repository archived) and will not receive maintenance, security updates, or compatibility fixes for newer Airflow or dbt versions. Consider using actively maintained alternatives before adopting this package.

Install

airflow-dbt on PyPI

pip install airflow-dbt

uv add airflow-dbt

poetry add airflow-dbt

Installing airflow-dbt

Before you install

Low install friction with a single runtime dependency on apache-airflow. However, the package is abandoned with no updates since the latest release on 2021-09-03, and the repository is archived—expect no maintenance, bug fixes, or compatibility updates.

License in practice

MIT license permits use, modification, and distribution with minimal restrictions, making it suitable for commercial and open-source projects alike.

Quickstart

pip install airflow-dbt

from airflow_dbt.operators.dbt_operator import DbtRunOperator, DbtTestOperator
from airflow import DAG

with DAG(dag_id='dbt') as dag:
    dbt_run = DbtRunOperator(task_id='dbt_run', dir='/path/to/dbt')
    dbt_test = DbtTestOperator(task_id='dbt_test', dir='/path/to/dbt')
    dbt_run >> dbt_test

dbt CLI must be installed and available on PATH or specified via dbt_bin argument; apache-airflow must be installed and configured.

Verify before relying

• Whether operators remain compatible with current Airflow and dbt CLI versions given the maintenance gap since 2021-09-03
• Whether dbt CLI must be pre-installed separately or is bundled as a dependency

Package facts

Evidence: airflow_dbt-0.4.0-py2.py3-none-any.whl

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