apache-airflow-providers-docker
Provider package apache-airflow-providers-docker for Apache Airflow
Decision gist · record as of 2026-08-14
Yes. This is an actively maintained, production-stable provider from the Apache Airflow project with no known vulnerabilities, low install friction, and a permissive license. Install it if you need to run Docker containers as Airflow tasks. It is a standard integration for containerized workloads in Airflow environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and Docker daemon access; Python 3.10 or later.
- Low friction install as a wheel.
- Actively maintained with a release 6 days old.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,138,675 downloads/mo, #3,262 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-docker
from airflow.providers.docker.operators.docker import DockerOperator
from airflow import DAG
with DAG('example') as dag:
task = DockerOperator(
task_id='docker_task',
image='my-image:latest',
command=['python', 'script.py']
)- Whether DockerOperator supports all Docker API features or has known limitations with specific image types or configurations.
- How the provider handles Docker daemon connectivity and socket mounting across different deployment environments.
What it is and what it does
This is an Apache Airflow provider package that adds Docker container execution capabilities to Airflow DAGs. It wraps the Docker Python client and integrates it with Airflow's task execution model, allowing you to define tasks that run arbitrary Docker images as part of your workflow orchestration. The package depends on apache-airflow, docker, python-dotenv, and apache-airflow-providers-common-compat.
Typical use is to instantiate a DockerOperator in your DAG definition, specifying a container image and command. The operator handles pulling the image, running the container, and reporting task status back to Airflow. This is useful when you want to isolate task dependencies, run workloads in different environments, or leverage existing containerized applications without modifying them for Airflow's Python runtime.
Use it for
- Run isolated Python or shell scripts in Docker containers without installing dependencies in the Airflow environment.
- Execute pre-built containerized applications (e.g., data processing tools, ML pipelines) as Airflow tasks.
- Orchestrate multi-language workflows by running containers with different language runtimes in a single DAG.
- Manage task resource limits and environment isolation by running tasks in separate containers.
- Integrate third-party containerized services into Airflow workflows without modifying their code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is an actively maintained, production-stable provider from the Apache Airflow project with no known vulnerabilities, low install friction, and a permissive license. Install it if you need to run Docker containers as Airflow tasks. It is a standard integration for containerized workloads in Airflow environments.
Install
apache-airflow-providers-docker on PyPI
Before you install
Low friction install as a wheel. Actively maintained with a release 6 days old. Requires Apache Airflow >=2.11.0, docker >=7.1.0, and python-dotenv >=0.21.0 as runtime dependencies.
Requires Apache Airflow >=2.11.0 and Docker daemon access; Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install apache-airflow-providers-docker
from airflow.providers.docker.operators.docker import DockerOperator
from airflow import DAG
with DAG('example') as dag:
task = DockerOperator(
task_id='docker_task',
image='my-image:latest',
command=['python', 'script.py']
)Verify before relying
- Whether DockerOperator supports all Docker API features or has known limitations with specific image types or configurations.
- How the provider handles Docker daemon connectivity and socket mounting across different deployment environments.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesapache-airflowapache-airflow-providers-common-compatdockerpython-dotenv |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,138,675 / month, #3,262 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/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_docker-4.5.9-py3-none-any.whl
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See also apache-airflow-providers-singularity · apache-airflow-task-sdk · apache-airflow-providers-git · apache-airflow-providers-arangodb · apache-airflow-providers-neo4j · apache-airflow-providers-jenkins · astronomer-starship · apache-airflow-providers-ftp · podman-compose · hera