"Large-scale data pipelines often rely on full historical reprocessing due to its conceptual simplicity. However, as datasets grow and update frequencies increase, this approach becomes computationally expensive and operationally fragile. This article presents a practical migration from historical batch processing to incremental Change Data Capture (CDC) using Apache Iceberg Copy-on-Write (COW) tables in AWS Glue 4 (Spark 3.3). The focus is on designing incremental semantics without native CDC or Merge-on-Read support, which reflects the constraints faced by many production environments today."
"1.2 Observed Limitations Empirically, historical processing leads to: Redundant computation: unchanged records are repeatedly processed Excessive I/O: full scans dominate job runtime Poor fault isolation: failures affect entire datasets High cost variance: compute cost grows linearly with data size In one production pipeline, daily processing involved scanning tens of gigabytes of data even though the effective data change rate was below 2%."
Large-scale Spark pipelines that rebuild target tables via full historical reprocessing incur redundant computation, excessive I/O, poor fault isolation, and linear cost growth as data volumes increase. One production example scanned tens of gigabytes daily despite under 2% effective change rate. Apache Iceberg provides snapshot isolation, file-level metadata, ACID MERGE INTO semantics, and time travel that enable incremental processing on immutable files. AWS Glue 4 (Spark 3.3) lacks native CDC and Merge-on-Read support in many deployments, constraining design choices. Copy-on-Write Iceberg tables become the practical option under these constraints. The migration focuses on designing incremental semantics within Glue 4 limitations.
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