Dagster
Dagster is an asset-centric data orchestration platform that tracks lineage, quality signals, and dependencies for every data asset. We assess it as an alternative to task-centric schedulers like Airflow.
Blurb
Dagster is the operational layer that structures how data is built, observed, and delivered, so both teams and AI agents can rely on it.
Summary
- Asset-centric model: pipelines are defined by the data assets they produce, not sequences of tasks.
- First-class integrations with dbt, Snowflake, and Fivetran; open source core with a managed Dagster+ tier.
- Strong fit for teams that care about lineage, freshness, and blast-radius analysis before failures reach downstream consumers.
- Acquisition watch: Dagster announced it is joining Prefect. Evaluate roadmap and support implications before adopting; this may affect the long-term independence of the platform.
| Consider | When |
|---|---|
| Use | Asset-heavy stacks (dbt + warehouse) needing lineage and observability built in |
| Skip | Simple cron-style job scheduling with no data-model needs |
Dagster is complementary to Databricks, not a competitor: Databricks is where data lives and gets computed, while Dagster orchestrates the pipelines that produce it. Databricks ships its own scheduler (Lakeflow/Jobs) for work inside its platform. Dagster is chosen when you want one orchestration view across Databricks plus dbt, Snowflake, and external systems; if everything lives in one platform, native tooling may suffice.
Details
- Open source core: https://github.com/dagster-io/dagster
- Docs: https://docs.dagster.io/
- Learning: Dagster University (https://courses.dagster.io/)
- Comparison pages: vs Airflow, vs dbt Cloud, vs Azure Data Factory, vs AWS Step Functions (on dagster.io)