Airflow is ideal when you need to implement complex workflows that involve multiple tasks with dependencies, conditions, and dynamic pipelines. For example, if you are working on a data pipeline that requires triggering data extraction from several APIs, processing that data with transformations, and loading it into multiple data targets based on different conditions, Airflow allows you to define these workflows more intricately than Meltano.
Meltano is ideal when you need a streamlined solution for extracting, loading, and transforming data in a unified environment. It is especially useful for teams looking to quickly implement a data pipeline without extensive engineering overhead. For example, if you're a small startup wanting to pull data from several APIs into your data warehouse quickly and affordably, Meltano would be a great fit, as it allows you to set up and manage your pipeline easily without the complexity of orchestrating tasks like Airflow would require.