What is Dagster used for?

Dagster is a Python data orchestration platform for building, scheduling and observing data pipelines around the data assets they produce; Dagster is now part of Prefect. It is a good fit for ETL and ELT pipelines, machine learning workflow orchestration, and managing data products as assets.

Is Dagster open source?

Yes. Dagster is open source under the Apache-2.0 licence. Its source code is on GitHub at dagster-io/dagster and is written mainly in Python.

Yes. Dagster is open source, so the software itself is free to use. A managed cloud version is also available, with paid plans from $10 per month.

Can I self-host Dagster?

Yes. Dagster can be self-hosted on your own server or infrastructure.

What is Dagster an alternative to?

Dagster is an open-source alternative to Astronomer and Azure Data Factory. Other open-source alternatives to Astronomer include Airflow, Prefect and Kestra.

Is Dagster actively maintained?

Yes. The most recent commit to Dagster was on 2 October 2026, and the latest release is 1.13.25, published on 1 October 2026. The project has 16k stars on GitHub.

Dagster: open-source alternative to Astronomer

About Dagster

Dagster is an orchestration platform for developing, running and observing data assets. Instead of thinking only in terms of tasks, teams define the tables, files and models their pipelines produce, and Dagster tracks how they are built, scheduled and monitored. It is written in Python and aimed at data engineering, analytics and machine learning workloads.

The product surface covers data orchestration, a data catalog, data quality checks, cost insights and many integrations, with an enterprise offering for larger organizations. Comparison pages on the website position it against Airflow, dbt Cloud, Azure Data Factory and AWS Step Functions, and topics include ETL, scheduling and MLOps. The homepage now announces that Dagster is part of Prefect, and points visitors looking for agentic orchestration or MCP support to Prefect.

The open-source core is Apache-2.0 licensed and can be self-hosted, while a managed cloud and enterprise plans are offered with pricing on the company site. Dagster University and documentation help with learning. It suits data teams that want a software-engineering-style approach to pipelines.

Key features

Asset-centric data orchestration
Scheduling and monitoring of pipelines
Data catalog and data quality features
Cost insights for pipelines
Integrations with common data tools
Pipelines developed in Python

Good fit for

→ETL and ELT pipelines
→Machine learning workflow orchestration
→Managing data products as assets

Built with: Python
Tags: data-orchestration; data-pipelines; python; etl; scheduler; mlops; data-engineering; observability; workflow

Dagster: questions and answers

What is Dagster used for?: Dagster is a Python data orchestration platform for building, scheduling and observing data pipelines around the data assets they produce; Dagster is now part of Prefect. It is a good fit for ETL and ELT pipelines, machine learning workflow orchestration, and managing data products as assets.
Is Dagster open source?: Yes. Dagster is open source under the Apache-2.0 licence. Its source code is on GitHub at dagster-io/dagster and is written mainly in Python.
Is Dagster free?: Yes. Dagster is open source, so the software itself is free to use. A managed cloud version is also available, with paid plans from $10 per month.
Can I self-host Dagster?: Yes. Dagster can be self-hosted on your own server or infrastructure.
What is Dagster an alternative to?: Dagster is an open-source alternative to Astronomer and Azure Data Factory. Other open-source alternatives to Astronomer include Airflow, Prefect and Kestra.
Is Dagster actively maintained?: Yes. The most recent commit to Dagster was on 2 October 2026, and the latest release is 1.13.25, published on 1 October 2026. The project has 16k stars on GitHub.