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Incremental loading is a data integration method that transfers only changed or new records since the last load. It reduces bandwidth, storage needs and source system load; common use cases include ETL/ELT, data warehouses and near-real-time replication. The approach requires robust change detection, error handling, timestamps and idempotency.
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Incremental loading transfers only new or changed data instead of reloading an entire dataset.
It arose from the need to process large datasets efficiently with short refresh times. Change data capture and tools such as Debezium expose source changes as a continuous stream.
Think of a water meter: remember how far you have read, detect changes from that point, process them, and advance a safe checkpoint. On failure, resume there rather than reload everything.
Inserted, updated, or deleted records become the units of work.
It marks the last safely processed position and enables resumption.
Repeating a change must not corrupt the target dataset.
Incremental loading reduces pipeline and replication load, runtime, and cost. It requires reliable change markers, failure handling, and deletion rules; late or lost events threaten completeness.
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