Apache Airflow can be used to build a data pipeline (ETL, Machine Learning, etc.) with task dependencies. It supports the scheduling of tasks and can handle task failures, so that certain actions will be triggered if a task results in an error: for example, issuing an alert, rerunning a task, or triggering alternative workflows. Also, thanks to parallelisation, the DAG can branch, so a task failure in one branch does not have to affect tasks in another.
Apache Airflow offers a user interface that provides close monitoring of the entire workflow as well as individual task performance over time. This is essential for the continuous improvement of the data pipeline and gives you a reliable and transparent basis to enforce SLAs. Apache Airflow easily scales with increasing workloads, and will also detect underperforming tasks for debugging.