Airflow is a great choice when you need to orchestrate complex data workflows that involve diverse tasks and dependencies. You would use Airflow when your pipelines need to include scheduled tasks, dynamic generation of workflows, and integration with various services through its extensive plugin system. A specific scenario where you would choose Airflow over Flyte is when you have a data pipeline that relies heavily on scheduled batch jobs from various sources, requiring intricate dependency management and failover strategies, which are better supported by Airflow's mature features.
Flyte is ideal for scenarios where you need to manage data workflows with complex dependencies and versioning, particularly in machine learning projects. For example, if you have a pipeline that includes training multiple models with varying hyperparameters and requires tracking data versions alongside model versions, Flyte's capabilities streamline this process more effectively than Airflow.