Incremental pipeline processing

Incremental processing limits work to new or changed data when the source, transformations, and target semantics support it. Streaming tables and incremental refreshes can reduce repeated scans for suitable workloads. The pipeline must still handle late data, corrections, deletes, and changes in transformation logic. A full refresh can be appropriate when logic changes invalidate previous outputs, but it has cost and operational impact. Understand which operations preserve incremental behavior and when state or dependencies require broader recomputation. Compare processing metrics and output correctness rather than assuming incremental always means faster. Practice task: identify which upstream correction would require reprocessing prior records in a monthly aggregate. Applying the pattern: Track progress from a durable source boundary, apply supported incremental transformations, and reconcile output after late changes or code updates. Checks and failure modes: An incremental job may be fast but incorrect if its watermark, update semantics, or correction policy discards relevant changes. Before production, test both a representative input and a boundary case. Confirm the expected row grain, permissions, recovery behavior, and observable output for the task. Record the configuration needed to reproduce the run and compare its result with the agreed data contract. Practice task: When might a broader refresh be necessary?