![]() Option 1: Lift and Shift from Airflow to Dagster Plus, the local development mode makes it easy to test pipelines before deployment."- Jatin Solanki, founder, Decube.io in Migrating from Apache Airflow to Dagster. The built-in tools for managing configurations, maintaining data quality, and visualizing data lineage are a godsend. The development and testing process has become streamlined and the data quality checks have drastically reduced the time I spend troubleshooting. "Transitioning to Dagster was a calculated risk that paid off. Luckily we have provided handy guides such as a ” Airflow vs Dagster concept map” and a “Learning Dagster from Airflow” tutorial. Developers familiar with Airflow will have to learn a slightly different set of concepts in Dagster. One challenge (and often underestimated) in making these types of transitions is the cognitive load involved in switching frameworks. The cognitive load of switching frameworks While a less common use case, we will touch on it at the end of this article. The main scenario for using the dagster-airflow adapter is to do a lift-and-shift migration of all your existing Airflow DAGs into Dagster.įor some use cases, you may also want to orchestrate Dagster job runs from Airflow. Enter dagster-airflow - the easy way to move from the old to the newĭagster-airflow is a new package that provides interoperability between Dagster and Airflow. But realistically, no team wants to manage two platforms, certainly not for long. Sure, it’s possible to run old legacy pipelines on Airflow and build anything new on Dagster. Still, porting over dozens or hundreds of Apache Airflow DAGs to Dagster, and refactoring them to take full advantage of Dagster’s Software-defined Assets was intimidating-enough to deter many teams for whom working on Airflow was painful, but not painful enough to make the switch. For example, one team cut the time to test a change in development from 10 minutes to under a minute. The difference in developer ergonomics, deployment, observability, testability and support for real CI/CD has had a significant impact on their ability to ship high-quality data pipelines that are maintainable, observable, and extensible. Since the release of Dagster 1.0 and our GA release of Dagster Cloud on August 9th 2022, many data teams have taken the plunge and switched from Airflow to Dagster. Dagster emerged as the Apache Airflow alternative. ![]() ![]() The frustrations with Airflow were well known, and the need for a better framework was widely appreciated. In the early days of Dagster, before we released Dagster V 1.0, there was a lot of interest from organizations running on Apache Airflow who were looking for a better orchestrator. Data platform migration - not an easy lift 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.įor reference, the dagster-airflow docs can be found here. With the release of dagster-airflow 1.1.17, data engineering teams have access to tooling that makes porting Airflow DAGs to Dagster much easier. ![]()
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