Description
Dagster is a data orchestration platform that helps engineers develop, produce, and observe data assets with reliability and scalability. It acts as the operational layer that structures how data is built, observed, and delivered, so both teams and AI agents can rely on it.
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The platform is asset-based: engineers build and run pipelines across any tool in their stack, and Dagster attaches lineage, dependencies, quality signals, and data health to every asset. This gives teams end-to-end visibility so they can catch problems early, understand their impact instantly, and diagnose issues faster. Dagster reports customer outcomes such as fresher business-critical data, faster developer onboarding, and reduced pipeline execution times.
Dagster positions itself as an AI-native DataOps platform, running on the operational context it already has, including assets, runs, lineage, freshness, failures, and automation history. With Compass, teams can build and ship governed data agents on top of trusted data workflows, and features like branch deployments let teams validate pipeline changes in a production-like environment before touching real data.
- CategoryMLOps & Model Deployment
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
Dagster's Core Features
Asset-based data orchestration across your stack
Build, schedule, and monitor reliable data pipelines
End-to-end lineage, dependencies, and data health per asset
Observability to catch problems early and assess impact
AI-native DataOps with operational context for agents
Compass for building governed data agents on trusted workflows
Branch deployments to validate changes before production
Reusable components, shared standards, and built-in guardrails
How to use Dagster?
Define assets: Model your data as assets that Dagster orchestrates.
Build pipelines: Connect tools across your stack into asset-based pipelines.
Schedule and run: Schedule pipelines and run them with full visibility.
Observe: Track lineage, dependencies, freshness, and data health for every asset.
Iterate safely: Use branch deployments to validate changes in a production-like environment.
Dagster's Use Cases
- Data pipeline orchestration
- Data observability
- Governed data agents
- Safe pipeline changes
