Incremental Batch Processing
Incremental batch processing is a batch execution pattern that processes only source data that is new or changed since the last successful run, rather than recomputing the whole dataset. It relies on partitions, watermarks, metadata, or control tables to decide what work is pending and to publish results safely.
Full batch recomputation becomes painful once datasets grow: every run scans old data, spends money on unchanged inputs, and lengthens recovery when something fails. The hard part is not merely skipping files. The job must know exactly which slices of data are complete enough to process, which have already been published, and how to recover if a run fails halfway through without losing or duplicating downstream records.
In practice, an incremental job separates discovery, transformation, and publication. It first finds candidate partitions, such as dates, hours, regions, or ingestion batches, by reading source metadata, a control table, target-table state, or comparing watermarks. It then runs the normal transformations only for those partitions. The output is written to a staging area, checked, and then used to replace, append to, or merge into the destination table.
The main trade-off is operational complexity. Incremental jobs must be idempotent, meaning rerunning the same partition should leave the destination in the same correct state. Append-only data is usually simpler, while mutable data needs keyed merges and explicit rules for late arrivals, corrections, deletes, and updates. A common misunderstanding is that partitioning alone makes a job safe; it only makes selective processing possible.
Engineers meet incremental batch processing in data lakes, warehouses, scheduled ETL jobs, table maintenance workflows, and backfills. The important production details are progress tracking, atomic publication, validation, and observability. Record progress only after the downstream write succeeds. Log the partitions processed, compare row counts, alert on missing or unusually large partitions, and keep enough run history to replay corrected ranges.
Common questions
- How does incremental batch processing differ from streaming?
- Incremental batch processing still runs as discrete jobs over bounded chunks of data, often on a schedule. Streaming processes records continuously or near-continuously as they arrive. The boundary can blur, but the operational model differs: incremental batch usually reasons about partitions, watermarks, and table replacement or merge steps, rather than a long-running event processor.
- What makes an incremental batch job safe to retry?
- A safe job is idempotent. It can process the same partition again without duplicating rows or leaving mixed old and new output. Common techniques include writing to a staging location, validating the result, then atomically replacing a partition or performing a deterministic merge. Progress should be marked only after the final write has succeeded.
- Should the job track processed data from the source or the destination?
- It depends on the reliability of each system and the failure modes you need to handle. Source metadata or ingestion watermarks are useful for discovering new work. Destination partitions or a control table are useful for proving what was actually published. Many robust designs use both: discover from the source, but commit progress after the destination update.