Apache Airflow vs Dagster
A side-by-side comparison of two self-hosted automation & workflows options — licensing, setup difficulty, resource needs, and what each one replaces.
Dagster
Data orchestrator for the full development lifecycle
| Feature | Apache Airflow | Dagster |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 100 · Excellent | 95 · Excellent |
| Category | Automation & Workflows | Automation & Workflows |
| License | Apache-2.0 | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Hard | Medium |
| Min. RAM | 4,096 MB | 2,048 MB |
| Deployment | docker, kubernetes, helm | docker, kubernetes, helm |
| GitHub stars | ★ 46,955 | ★ 16,195 |
| First released | 2015 | 2019 |
| Replaces | AWS Step Functions, Azure Data Factory | AWS Step Functions, Azure Data Factory |
What are Apache Airflow and Dagster?
Apache Airflow
Apache Airflow is a platform to author, schedule and monitor workflows as directed acyclic graphs of tasks. It is widely used for data engineering pipelines and complex job orchestration.
- Workflows defined as Python code
- Rich scheduling and dependency management
- Extensive operator and provider ecosystem
- Web UI for monitoring DAGs
Dagster
Dagster is an orchestration platform for data assets that emphasizes testability and observability. It models pipelines around the assets they produce and can be fully self-hosted.
- Asset-oriented orchestration
- Built-in testing and typing
- Integrated data lineage
- Web-based control plane
Apache Airflow vs Dagster: key differences
Both projects are written in Python. Dagster is the lighter option, starting around 2,048 MB of RAM against 4,096 MB for Apache Airflow. Apache Airflow is the more established project (first released 2015), while Dagster arrived in 2019. Apache Airflow has the considerably larger community, at 46,955 GitHub stars versus 16,195.
Why pick each one
Choose Apache Airflow if…
- Industry standard for data pipelines
- Huge ecosystem
Watch out for
- Heavy to operate
- Steep learning curve
Choose Dagster if…
- Strong developer tooling
- Great for data-centric teams
Watch out for
- Conceptual model takes time to learn
Frequently asked questions
Is Apache Airflow or Dagster better?
Neither is universally better. Apache Airflow has the larger community, while Dagster is simpler to set up (medium difficulty). Choose based on the comparison table above and your own setup.
Are Apache Airflow and Dagster free and open-source?
Yes. Apache Airflow is licensed under Apache-2.0 and Dagster under Apache-2.0. Both can be self-hosted at no software cost.
Can I run Apache Airflow and Dagster with Docker?
Apache Airflow: yes. Dagster: yes.
Which is lighter on resources, Apache Airflow or Dagster?
Dagster has the smaller minimum footprint at 2,048 MB of RAM, compared to about 4,096 MB for Apache Airflow. Real-world usage depends on library size, user count, and enabled features.