dagster open source analysis
An orchestration platform for the development, production, and observation of data assets.
Project overview
⭐ 14708 · Python · Last activity on GitHub: 2026-01-06
Why it matters for engineering teams
Dagster addresses the challenge of managing complex data workflows by providing a unified platform for building, orchestrating, and monitoring data pipelines. It is particularly suited for machine learning and AI engineering teams who need a production ready solution to ensure data reliability and observability throughout the pipeline lifecycle. The project is mature and widely adopted, with strong support for Python and integration with common data tools, making it reliable for production environments. However, Dagster may not be the best fit for teams seeking a lightweight scheduler or those with very simple ETL needs, as its comprehensive feature set can introduce additional complexity and overhead.
When to use this project
Dagster is a strong choice when engineering teams require a self hosted option for orchestrating complex data workflows with observability and metadata tracking. Teams should consider alternatives if their workflows are simple or if they prefer a fully managed cloud service without the need for extensive customisation.
Team fit and typical use cases
Machine learning and AI engineers benefit most from Dagster as it helps them build and maintain robust data pipelines critical for model training and deployment. Data engineers also use it to automate and monitor ETL processes within production systems. This open source tool for engineering teams often appears in products that rely on scalable data integration and workflow automation to support analytics and machine learning operations.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.