What is the purpose of "checkpointing" in streaming applications on Databricks?

Checkpointing in streaming applications on Databricks is primarily aimed at ensuring fault tolerance and data processing reliability. When processing streams of data, there is a possibility of failures or interruptions. Checkpointing creates a stable backup of the current state of the streaming application, allowing the system to recover from failures without losing any processed data.

In the event of a failure, the application can seamlessly restart from the last checkpoint, which contains information about the position of the data that has already been processed. This mechanism is crucial for maintaining data consistency and reliability, as it allows for uninterrupted data processing even amid system failures, supporting the integrity of long-running streaming workloads.

The other options, while they touch on aspects related to data processing, do not specifically capture the primary role of checkpointing. For instance, minimizing data discrepancies or enhancing storage capacity may be beneficial for overall system performance, but these are not directly addressed by the checkpointing process. Similarly, while faster job completion times are relevant in the broader context of performance optimization, checkpointing serves a distinct purpose focused on recovery and reliability.