airflow-dbt
Apache Airflow integration for dbt
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- dbt CLI must be installed and available on PATH or specified via dbt_bin argument; apache-airflow must be installed and configured.
- 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 · maintenance · safety
MIT (permissive) — MIT license permits use, modification, and distribution with minimal restrictions, making it suitable for commercial and open-source projects alike.
last release 2021-09-03 (1806 days) · last repo commit 2024-05-17 · 415 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 538,893 downloads/mo, #6,111 on PyPI
Alternatives
Verify before relying
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- 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
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 on it.
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
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.
dbt CLI must be installed and available on PATH or specified via dbt_bin argument; apache-airflow must be installed and configured.
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_testVerify 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
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageapache-airflow |
| Maintenance | Abandoned 1,806 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 538,893 / month, #6,111 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 :: MIT LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.7 |
Evidence: airflow_dbt-0.4.0-py2.py3-none-any.whl
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See also airflow-dbt-python · astronomer-cosmos · airflow-mcd · apache-airflow-providers-dbt-cloud · apache-airflow-providers-arangodb · apache-airflow-providers-git · dbt · apache-airflow-microsoft-fabric-plugin · airflow-provider-hightouch · apache-airflow-providers-microsoft-fabric