Dagster

Dagster treats data as first-class citizens. Instead of defining tasks that run in order (Airflow-style), you define data assets and their dependencies — Dagster figures out what to run and when. This asset-centric model is a genuinely better mental model for data engineering.

The asset graph, built-in data lineage, and integrated observability make debugging data pipelines actually manageable. Dagster Cloud offers serverless execution. Compared to Airflow (task-centric, battle-tested, more complex), Dagster is more opinionated and modern. Compared to Prefect (Python-first, event-driven), Dagster's asset model is more structured. Compared to dbt (SQL transforms only), Dagster orchestrates everything.

Use this when you're building data pipelines and want clear lineage from source to dashboard. Skip this if you're doing simple ETL that Airflow handles fine — switching has real migration cost.

The catch: the asset-centric model has a learning curve if you're coming from task-based orchestrators. And Dagster's opinionated approach means less flexibility — if your workflow doesn't fit the asset model, you'll fight the framework. Apache 2.0 license.