dagster-io/dagster
An orchestration platform for the development, production, and observation of data assets.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest today, with 406 contributors on GitHub.
Use Cases & Benefits
- Orchestrates data pipelines and assets through a declarative Python programming model with integrated lineage and observability.
- Provides a unified control plane combining development, production, and monitoring with built-in metadata management and testability.
- Use for developing and maintaining complex data assets like tables, ML models, and reports with automated scheduling and updates.
- Use for scaling multi-tenant data workflows in production environments with robust orchestration and integration capabilities.
- Use for centralizing metadata and lineage tracking to improve data quality, diagnose issues, and optimize pipeline performance.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.
It is designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.
With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.
Here is an example of a graph of three assets defined in Python:
Dagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.
If you're new to Dagster, we recommend checking out the docs or following the hands-on tutorial.
Dagster is available on PyPI and officially supports Python 3.9 through Python 3.13.
You can find the full Dagster documentation here, including the Quickstart guide.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
2.4K+166
Open issues
2.6K+13
Compared with peers in Analytics
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| TwiN/gatus | 12.7K | +210 | 64 | 12 days ago |
| algorithm-visualizer/algorithm-visualizer | 49.5K | +74 | 75 | — |
| sinaptik-ai/pandas-ai | 23.9K | — | 40 | 391 days ago |
| cube-js/cube | 21.1K | — | 65 | 1 day ago |
| dagster-io/dagster | 16.4K | — | 45 | today |
| primefaces/primeng | 12.5K | — | 52 | 58 days ago |
| Category median | 21.1K | +142 | 64 | — |
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