Why data teams are switching from Airflow to Dagster
Dagster’s Software Defined Assets provide an intuitive framework for collaboration with data practitioners across the enterprise as you build out your data platform. You can focus on delivering critical data assets, not on the tasks of pipelines.
Airflow is task-centric and does not provide asset-aware features or a coherent Python API. It is typically implemented after pipelines have been designed to trigger the required tasks.
Better testing and debugging
Dagster is designed for use at every stage of the data development lifecycle. It’s built to facilitate local development, unit testing, CI, code review, staging environments, and debugging.
Airflow makes pipelines hard to test, develop, and review outside of production deployments. Many teams working on Airflow do their final testing in production as it does not provide branch deployments.
Dagster is cloud- and container-native: dependencies are easy to manage and upgrades are smooth. Dagster is designed for today's data infrastructure (ECS, K8s, Docker). Dagster Cloud provides a turnkey hosting solution.
Isolating dependencies and provisioning infrastructure with Airflow is complex and time consuming. Several commercial solutions will provide support (Astro, AWS MWAA, Qubole, etc.)
Dagster has a growing community of forward-thinking engineers who see the value of our declarative framework. The Dagster engineering team is directly involved in supporting both open source and Dagster Cloud users.
Airflow has a very large community of users as the technology has been around for much longer. Individual vendors may provide support on their instance.
Migrating off Airflow is now a breeze
Dagster provides tooling that makes porting Airflow DAGs to Dagster much easier. Data teams looking for a radically better developer experience can now easily transition away from legacy imperative approaches and adopt a modern declarative framework that provides excellent developer ergonomics.