How to Add Error Handling and Monitoring to a Data Pipeline

Most data pipeline guides cover the happy path: extract data, transform it, load it to the destination. What they skip is everything that happens when the path isn't happy: the API that returns unexpected data, the transformation that fails partway through, the destination write that times out after writing 400 of 500 records. This guide is the other half: how to handle errors correctly, how to monitor pipeline health, and how to make your pipeline re-runnable after a failure. Step 1: Categorize