dagster-docker

What it is and what it does

dagster-docker is a Dagster integration that allows you to run data assets and pipelines inside Docker containers as part of your Dagster orchestration. It bridges Dagster's declarative asset model with Docker's containerization, letting you define assets in Python and have them execute in isolated container environments managed by Dagster.

The package depends on dagster (the core orchestration framework), docker (the Python Docker client), and docker-image-py. It is designed for teams that want to containerize their data workflows while keeping them coordinated through Dagster's central orchestration, lineage tracking, and observability layer. It's part of Dagster's broader ecosystem of integrations for modern data infrastructure.

Use it for:

• Run Dagster assets in Docker containers to isolate dependencies and ensure reproducible execution across environments.
• Orchestrate multi-container data pipelines where different assets require different runtime environments or system packages.
• Deploy data workflows to Kubernetes or other container platforms by containerizing Dagster job execution.
• Integrate legacy or third-party tools that are only available as Docker images into a Dagster-orchestrated data platform.

Worth the install?

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

Integrates Dagster data orchestration with Docker, enabling containerized execution of data pipelines and assets within Dagster's orchestration framework.

Yes. This is a straightforward, actively maintained integration for a popular orchestration framework. Install it if you need to run Dagster assets in Docker containers. The low install friction, permissive license, and active maintenance make it a safe choice. No known security vulnerabilities.

Install

dagster-docker on PyPI

pip install dagster-docker

uv add dagster-docker

poetry add dagster-docker

Installing dagster-docker

Before you install

Low friction installation with three runtime dependencies. Actively maintained with a release on 2026-08-14 and 15996 GitHub stars, indicating stable ongoing development.

License in practice

Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions.

Quickstart

pip install dagster-docker

import dagster as dg
from dagster_docker import DockerExecutor

@dg.asset
def my_asset():
    return "data"

defs = dg.Definitions(
    assets=[my_asset],
    executor=DockerExecutor()
)

Requires Docker to be installed and running on the system where Dagster executes jobs; Python 3.10 or later.

Verify before relying

• Specific Docker image requirements or registry configuration options beyond the runtime dependencies listed.
• Whether container networking, volume mounting, or resource limits are configurable through this integration.
• Performance characteristics or overhead of containerized execution compared to in-process execution.

Package facts

Evidence: dagster_docker-0.29.18-py3-none-any.whl

Tags

More Distributed Computing packages