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A collection of Airflow operators, hooks, and utilities to execute dbt commands

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PyPI version CI testing Code style: black Test coverage Documentation

A collection of Airflow operators, hooks, and utilities to execute dbt commands.

Read the documentation for examples, installation instructions, and more details.



Before using airflow-dbt-python, ensure you meet the following requirements:

  • A dbt project using dbt-core version 1.4.0 or later.

  • An Airflow environment using version 2.2 or later.

    • If using any managed service, like AWS MWAA, ensure your environment is created with a supported version of Airflow.
    • If self-hosting, Airflow installation instructions can be found in their official documentation.
  • Running Python 3.8 or later in your Airflow environment.


Even though we don't impose any upper limits on versions of Airflow and dbt, it's possible that new versions are not supported immediately after release, particularly for dbt. We recommend testing the latest versions before upgrading and reporting any issues.


Older versions of Airflow and dbt may work with airflow-dbt-python, although we cannot guarantee this. Our testing pipeline runs the latest dbt-core with the latest Airflow release, and the latest version supported by AWS MWAA.

From PyPI

airflow-dbt-python is available in PyPI and can be installed with pip:

pip install airflow-dbt-python

As a convenience, some dbt adapters can be installed by specifying extras. For example, if requiring the dbt-redshift adapter:

pip install airflow-dbt-python[redshift]

From this repo

airflow-dbt-python can also be built from source by cloning this GitHub repository:

git clone
cd airflow-dbt-python

And installing with Poetry:

poetry install


Add airflow-dbt-python to your requirements.txt file and edit your Airflow environment to use this new requirements.txt file, or upload it as a plugin.

Read the documentation for more a more detailed AWS MWAA installation breakdown.

In other managed services

airflow-dbt-python should be compatible with most or all Airflow managed services. Consult the documentation specific to your provider.

If you notice an issue when installing airflow-dbt-python in a specific managed service, please open an issue.


airflow-dbt-python aims to make dbt a first-class citizen of Airflow by supporting additional features that integrate both tools. As you would expect, airflow-dbt-python can run all your dbt workflows in Airflow with the same interface you are used to from the CLI, but without being a mere wrapper: airflow-dbt-python directly communicates with internal dbt-core classes, bridging the gap between them and Airflow's operator interface. Essentially, we are attempting to use dbt as a library.

As this integration was completed, several features were developed to extend the capabilities of dbt to leverage Airflow as much as possible. Can you think of a way dbt could leverage Airflow that is not currently supported? Let us know in a GitHub issue!

Independent task execution

Airflow executes Tasks independent of one another: even though downstream and upstream dependencies between tasks exist, the execution of an individual task happens entirely independently of any other task execution (see: Tasks Relationships).

In order to work with this constraint, airflow-dbt-python runs each dbt command in a temporary and isolated directory. Before execution, all the relevant dbt files are copied from supported backends, and after executing the command any artifacts are exported. This ensures dbt can work with any Airflow deployment, including most production deployments as they are usually running Remote Executors and do not guarantee any files will be shared by default between tasks, since each task may run in a completely different environment.

Download dbt files from a remote storage

The dbt parameters profiles_dir and project_dir would normally point to a directory containing a profiles.yml file and a dbt project in the local environment respectively (defined by the presence of a dbt_project.yml file). airflow-dbt-python extends these parameters to also accept an URL pointing to a remote storage.

Currently, we support the following remote storages:

  • AWS S3 (identified by a s3 scheme).

  • Remote git repositories, like those stored in GitHub (both https and ssh schemes are supported).

  • If a remote URL is used for project_dir, then this URL must point to a location in your remote storage containing a dbt project to run. A dbt project is identified by the prescence of a dbt_project.yml, and contains all your resources. All of the contents of this remote location will be downloaded and made available for the operator. The URL may also point to an archived file containing all the files of a dbt project, which will be downloaded, uncompressed, and made available for the operator.

  • If a remote URL is used for profiles_dir, then this URL must point to a location in your remote storage that contains a profiles.yml file. The profiles.yml file will be downloaded and made available for the operator to use when running. The profiles.yml may be part of your dbt project, in which case this argument may be ommitted.

This feature is intended to work in line with Airflow's description of the task concept:

Tasks don’t pass information to each other by default, and run entirely independently.

We interpret this as meaning a task should be responsible of fetching all the dbt related files it needs in order to run independently, as already described in Independent Task Execution.

Push dbt artifacts to XCom

Each dbt execution produces one or more JSON artifacts that are valuable to produce meta-metrics, build conditional workflows, for reporting purposes, and other uses. airflow-dbt-python can push these artifacts to XCom as requested via the do_xcom_push_artifacts parameter, which takes a list of artifacts to push.

Use Airflow connections as dbt targets (without a profiles.yml)

Airflow connections allow users to manage and store connection information, such as hostname, port, username, and password, for operators to use when accessing certain applications, like databases. Similarly, a dbt profiles.yml file stores connection information under each target key. airflow-dbt-python bridges the gap between the two and allows you to use connection information stored as an Airflow connection by specifying the connection id as the target parameter of any of the dbt operators it provides. What's more, if using an Airflow connection, the profiles.yml file may be entirely omitted (although keep in mind a profiles.yml file contains a configuration block besides target connection information).

See an example DAG here.


Airflow running in a managed environment

Although dbt is meant to be installed and used as a CLI, we may not have control of the environment where Airflow is running, disallowing us the option of using dbt as a CLI.

This is exactly what happens when using Amazon's Managed Workflows for Apache Airflow or MWAA: although a list of Python requirements can be passed, the CLI cannot be found in the worker's PATH.

There is a workaround which involves using Airflow's BashOperator and running Python from the command line:

from airflow.operators.bash import BashOperator

BASH_COMMAND = "python -c 'from dbt.main import main; main()' run"
operator = BashOperator(

But it can get cumbersome when appending all potential arguments a dbt run command (or other subcommand) can take.

That's where airflow-dbt-python comes in: it abstracts the complexity of interfacing with dbt-core and exposes one operator for each dbt subcommand that can be instantiated with all the corresponding arguments that the dbt CLI would take.

An alternative to airflow-dbt that works without the dbt CLI

The alternative airflow-dbt package, by default, would not work if the dbt CLI is not in PATH, which means it would not be usable in MWAA. There is a workaround via the dbt_bin argument, which can be set to "python -c 'from dbt.main import main; main()' run", in similar fashion as the BashOperator example. Yet this approach is not without its limitations:

  • airflow-dbt works by wrapping the dbt CLI, which makes our code dependent on the environment in which it runs.
  • airflow-dbt does not support the full range of arguments a command can take. For example, DbtRunOperator does not have an attribute for fail_fast.
  • airflow-dbt does not offer access to dbt artifacts created during execution. airflow-dbt-python does so by pushing any artifacts to XCom.


Currently, the following dbt commands are supported:

  • clean
  • compile
  • debug
  • deps
  • docs generate
  • ls
  • parse
  • run
  • run-operation
  • seed
  • snapshot
  • source
  • test


All example DAGs are tested against the latest Airflow version. Some changes, like modifying import statements or changing types, may be required for them to work in other versions.

import datetime as dt

import pendulum
from airflow import DAG

from airflow_dbt_python.operators.dbt import (

args = {
    "owner": "airflow",

with DAG(
    schedule="0 0 * * *","UTC").add(days=-1),
    tags=["example", "example2"],
) as dag:
    dbt_test = DbtTestOperator(

    dbt_seed = DbtSeedOperator(
        select=["/path/to/first.csv", "/path/to/second.csv"],

    dbt_run = DbtRunOperator(

    dbt_test >> dbt_seed >> dbt_run

More examples can be found in the examples/ directory and the documentation.


See the development documentation for a more in-depth dive into setting up a development environment, running the test-suite, and general commentary on working on airflow-dbt-python.


Tests are run with pytest, can be located in tests/. To run them locally, you may use Poetry:

poetry run pytest tests/ -vv


This project is licensed under the MIT license. See LICENSE.

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