Apache Airflow vs Dagster
A side-by-side comparison of two Data Pipeline AI agents — to help you pick the right one.
Apache Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. It uses directed acyclic graphs (DAGs) to define tasks and dependencies, with a built-in scheduler and web-based UI for visualization and management.
Dagster
Dagster is an open-source data orchestrator designed for building, testing, and running data pipelines. It focuses on developer productivity, enabling users to define pipelines with dependencies, track data lineage, and monitor execution in development and production environments.
| Apache Airflow | Dagster | |
|---|---|---|
| Category | Data Pipeline | Data Pipeline |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
Apache Airflow: what it solves
It automates and orchestrates complex data pipelines, ensuring tasks execute in the correct order and recover from failures.
Dagster: what it solves
It simplifies the creation and maintenance of complex data workflows by providing a framework for managing dependencies, testing, and observability across pipelines.