Dagster

What is Dagster?

Dagster is an open source orchestrator for building, scheduling, and observing data pipelines in Python. Instead of defining pipelines as a chain of tasks, you declare the data assets you want to exist — a table, a dataset, an ML model — as Python functions, and Dagster works out the dependencies, runs them in order, and tracks their lineage.

This asset-first model is what sets it apart from older task-based orchestrators. The whole platform is Apache-2.0 licensed and free to self-host.

What is Dagster best for?

Dagster fits data engineering teams that want to treat pipelines as testable, versioned software rather than a loose collection of cron jobs. It shines when you care about data lineage and observability — knowing what produced a table, when it last ran, and what breaks downstream if it fails. It’s a strong fit for modern data-stack workflows that combine dbt, Spark, warehouses, and Python transforms under one control plane.

What can Dagster do?

• Software-defined assets — declare tables, datasets, and models as Python functions; Dagster resolves dependencies and builds a full lineage graph automatically.
• Rich scheduling and automation — cron schedules, sensors that trigger on external events, and declarative freshness-based automation.
• Partitions and backfills — model time- or category-partitioned data and re-run just the slices you need.
• Built-in web UI — inspect the asset graph, run history, logs, and materialization status.
• First-class testability — run assets locally, unit-test them, and stage changes before production.
• Wide integrations — native support for dbt, Spark, Snowflake, BigQuery, Airbyte, Pandas, and much of the modern data stack.
• Typed I/O and config — strong typing on inputs, outputs, and run configuration to catch errors early.

Where does Dagster fall short?

• Asset-first thinking is a paradigm shift. Engineers coming from task-DAG tools like Apache Airflow expect to think in tasks and schedules; Dagster wants you to think in assets and materializations. It’s a genuinely different mental model, and the upfront rethinking is the most common complaint.
• Python only. Pipelines are authored in Python, so it’s a poor fit for teams that want a language-agnostic or YAML/SQL-first orchestrator — Kestra is closer to that.
• The best governance features are cloud-only. Column-level lineage, SSO/SAML, RBAC, audit logs, and alerting live in the paid Dagster+ cloud tiers, not the open source core.
• Smaller ecosystem than Airflow. The asset model is powerful, but the community and third-party integration library are still smaller than Airflow’s long-established one.

Is Dagster free?

Yes — the core Dagster orchestrator is Apache-2.0 licensed and free to self-host, with no user or pipeline limits. The open source version includes asset-based orchestration, scheduling, sensors, partitions, backfills, and the web UI.

The paid product is Dagster+, a managed cloud edition. It starts at $10/month (Solo) and $100/month (Starter) on pay-as-you-go credits, with custom Pro pricing for enterprises. Dagster+ adds serverless compute, an advanced data catalog with column-level lineage, SSO/SAML, RBAC, audit logs, and proactive monitoring on top of the open source engine.

What does Dagster replace?

Dagster is an open source alternative to managed and proprietary data pipeline tools like Azure Data Factory, AWS Step Functions, and Informatica. It also competes directly with other open source orchestrators — Prefect, Apache Airflow, and Temporal — with its asset-centric approach as the main differentiator.

FAQ

Is Dagster open source? Yes. Dagster’s core orchestration engine is fully open source under the Apache-2.0 license, and its source is on GitHub. Only the managed Dagster+ cloud service and some governance features are paid.

Can I self-host Dagster for free? Yes. You can run the full open source Dagster platform on your own infrastructure at no cost, with no limits on users or pipelines. You only pay if you choose the hosted Dagster+ service.

Is Dagster a good alternative to Airflow? For teams that value data lineage, local testing, and an asset-based model, yes. Dagster offers a more modern developer experience than Airflow, though Airflow has a larger community and more third-party integrations.

What do I need to run Dagster? Python 3.9–3.14 and pip (or uv). You install the dagster and dagster-webserver packages, and for production you typically add a database and a deployment target such as Docker or Kubernetes.