A feature-by-feature comparison of Dagster and Apache Airflow for data orchestration. Airflow, built in 2014, struggles with local development, debugging, data lineage visibility, scalability, containerization, and CI/CD due to its task-centric, monolithic architecture. Dagster addresses each limitation through asset-oriented design, structured logging, native Kubernetes and Docker support, and built-in CI/CD. Dagster+ extends the platform with a data catalog, column-level lineage, RBAC, automated data quality checks, and external asset integration. The post also notes that Dagster and Airflow can coexist, with a migration path available via the Airlift tool.
