Dagster

A Python orchestration platform built around data assets, the tables, files and models your pipelines produce, with lineage, schedules and observability.

16.4k stars, 2.4k forks Python On GitHub since April 2018

Visit website View on GitHub How to run it

What is Dagster?

Dagster models a data platform as assets rather than tasks: you declare in Python which tables, files or machine-learning models exist and what each depends on, and Dagster works out what to run and when. Its web UI shows lineage between assets, run history, freshness and data checks. It integrates with common tools of the modern data stack and runs locally, on your servers, on Kubernetes or in the paid Dagster+ cloud.

Good fit if

  • Your data team thinks in tables and models rather than in jobs and wants lineage built in.
  • You want to test pipelines locally in Python before deploying them.
  • You run Kubernetes: a Helm chart is part of the repository.

Look elsewhere if

  • You already have a large Airflow estate and many operators: Apache Airflow.
  • You prefer YAML workflows over Python: Kestra.

Before you adopt it

Dagster is Apache-2.0, with no paid parts in the repository; Dagster+ is the company's paid hosted product. The usual self-hosted setup needs PostgreSQL next to the Dagster services.

Drafted with AI from the project's README and repository data, then checked against that data on 30 October 2026. Report a correction

Dagster health: is it still maintained?

68 of 100, actively developed

  • Activity
    349 commits in the last 90 days, the latest on 30 October 2026.
    30 / 30
  • Contributors
    11 people wrote the latest 100 commits; the most active wrote 28% of them.
    14 / 20
  • Releases
    15 releases in the last 12 months; the latest, 1.13.28, on 30 October 2026.
    19 / 20
  • Replies to issues
    Maintainers answered 0% of recent issues.
    0 / 20
  • Security practices
    OpenSSF Scorecard 4.8 of 10.
    5 / 10

Read from GitHub on 1 November 2026. How the score works

Dagster license: is it open source?

Dagster uses Apache-2.0, an open-source license approved by the OSI.

  1. April 2018First published on GitHub.
  2. TodayApache-2.0.

How to run Dagster yourself

Docker
Dockerfile or compose file in the repository
Compose file
examples/deploy_docker/docker-compose.yml: 4 services (postgres)
Setup effort
3 of 5, judged from the compose file
Kubernetes
Helm chart in the repository
Desktop and mobile
Builds for macOS, Linux, including ARM in GitHub releases
Interface languages
English only

Install instructions: dagster.io or the README on GitHub.

Alternatives to Dagster

  • Apache AirflowApache Airflow - A platform to programmatically author, schedule, and monitor workflows 87 health · Apache-2.0 · 47.3k stars
  • KestraOrchestrate everything, everywhere. The open-source control plane for workflows across data, infrastructure,… 85 health · Apache-2.0 · 30.1k stars
  • PrefectPrefect is a workflow orchestration framework for building resilient data pipelines in Python. 80 health · Apache-2.0 · 24.1k stars
  • Apache DolphinSchedulerApache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow… 83 health · Apache-2.0 · 14.6k stars
  • Argo WorkflowsWorkflow Engine for Kubernetes 86 health · Apache-2.0 · 17.1k stars

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Questions about Dagster

Is Dagster open source?

Yes. Dagster is published under Apache-2.0, an OSI-approved open-source license.

Is Dagster still maintained?

Its health score is 68 of 100 (actively developed). It had 349 commits in the last 90 days, the latest on 30 October 2026, and its latest release came out on 30 October 2026.

How do I run Dagster?

You can run it with Docker, on Kubernetes with the Helm chart in the repository, as an app for macOS, Linux, including ARM. The README on GitHub has the exact steps.

What are the alternatives to Dagster?

Close alternatives in the Workflow automation category include Apache Airflow, Kestra, Prefect.