Apache Airflow vs Dagster
Side-by-side comparison to help you choose the best tool.
Apache Airflow
freeApache Airflow is an open-source workflow orchestration platform for authoring, scheduling, and monitoring data pipelines as directed acyclic graphs (DAGs). Originally created at Airbnb, it has become the industry standard for workflow scheduling with a massive community and thousands of providers. Airflow supports complex dependencies, flexible pipeline generation, and integrates with virtually every data tool.
Dagster
freemiumDagster is a data orchestration platform for building, observing, and operating data pipelines with an asset-centric approach. It models data pipelines as software-defined assets, making it easy to understand data lineage and dependencies. Dagster has deep integration with dbt, Spark, and modern data stack tools, and provides a rich UI for pipeline observation.
| Feature | Apache Airflow | Dagster |
|---|---|---|
| Pricing | free | freemium |
| Category | - | - |
| Rating | 4.4 | 4.5 |
| Best For | Data engineering teams needing a battle-tested, highly extensible workflow scheduler | Data platform teams building complex pipelines with modern data stack tools |
| Views | 69 | 63 |
Pros
- Industry standard with massive community
- Enormous ecosystem of providers
- Highly flexible and extensible
Cons
- Complex setup and maintenance
- Not ideal for real-time or streaming workflows
Pros
- Asset-centric model improves data understanding
- Excellent dbt integration
- Strong type system for pipeline safety
Cons
- Steeper learning curve than Prefect
- Resource-intensive for small teams
- DAG-based workflow scheduling
- Vast provider ecosystem
- Dynamic pipeline generation
- Web UI for monitoring
- Backfill and catchup capabilities
- Software-defined assets
- Data lineage tracking
- dbt integration
- Type-safe pipeline development
- Asset materialisation monitoring