Checkpoint for recovery
A robust ETL pipeline is not just about moving data from point A to point B efficiently; it’s also about ensuring that the pipeline can recover gracefully from failures and ensure data integrity throughout the process. To accomplish this, effective checkpointing needs to be incorporated with logging practices.
A “checkpoint” in the ETL process is a point in the data flow where key data cleansing and transformation processes “bookmark” the output data after each manipulation is stored in a temporary location. Thus, in the event of a failure, once the precise point of failure is identified, you can restart the ETL process from the last successful checkpoint, instead of from the beginning. This approach not only saves time and computational resources but also helps maintain data integrity by reducing the risk of duplicate or missed data. Using the same logging instance we defined earlier in this chapter, we can apply the same...
