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    How is this different from Airflow?

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      I just read their documentation. It appears that from their perspective, Airflow deals with operations and dependencies between operations, while Dagster derives “solid” (their name for operations)’s dependencies from their inputs / outputs.

      In this way, it can drive the same operations with different data from local development environment the same way as it is deployed in your ETL pipeline, much easier to develop / debug. Since it only cares about data dependencies, input artifacts and output artifacts can be managed by Dagster too, hence, it is easier to retry without worrying about side-effect.