Why data teams are switching from Airflow to Dagster
Dagster’s Software Defined Assets provide an intuitive framework for collaboration across the enterprise. 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 facilitates local development, unit testing, CI, code review, staging, and debugging.
Airflow pipelines are harder to test and review outside of production deployments. Many teams working on Airflow end up doing their final testing in production.
Dagster is cloud- and container-native, and designed for today's data infrastructure (ECS, K8s, Docker). Dependencies are easy to manage and upgrades are smooth. Dagster Cloud provides a turnkey hosting solution.
Isolating dependencies and provisioning infrastructure with Airflow is complex and time consuming.