Handling Failed Batch Loads

In data process management, two key strategies emerge: Reverting data to previous state and Configuring exception handling. While databases offer roll-back features for stability, ADF does not have built-in support for this. Instead, ADF focuses on consistency checks and fault tolerance settings to ensure data integrity.

For exception handling, connecting activities based on “success,” “failure,” “completion,” or “skip” outcomes provides control. Failed actions trigger fallback plans or alerts, ensuring pipeline continuity. Knowing about these mechanisms empowers smoother data workflows in both development and operational scenarios.

Note

This section primarily focuses on the Handle failed batch loads concept of the DP-203: Data Engineering on Microsoft Azure exam. The topic of what to do when batch data workloads fail has already been covered in Chapter 5, Developing a Batch Processing Solution...