Airflow is often chosen for its robustness and extensive community support, making it ideal for teams needing a stable workflow orchestration tool. A scenario to use Airflow might be when an organization has several pre-built integrations and plugins that work seamlessly with Airflow, making it easier to set up complex workflows quickly.
Dagster is particularly useful when your data pipelines require strong data type enforcement and need to validate data throughout the pipeline. It is ideal for scenarios where you have complex data transformations and want to ensure data quality, such as building a data processing pipeline that involves multiple steps of aggregating and cleaning data before loading it into a warehouse.