Airflow is best used for orchestrating complex workflows with dependencies among various tasks. For example, if you need to run a series of data processing jobs that depend on each other (like ETL processes where data extraction depends on a successful load), Airflow is more suited for this. You would use Airflow over NiFi when your tasks require precise scheduling and dependencies, especially in scenarios where the workflows become complex.
Apache NiFi is an excellent choice when you need to handle real-time data flows, especially in scenarios where data needs to be ingested from multiple sources on the fly, transformed, and sent to various destinations. For example, if you are collecting sensor data from Internet of Things (IoT) devices that need immediate processing and routing to different analytics services, NiFi would be more suitable than Airflow.