Using Iceberg Partition Overwrite for Data Correction in Spark Pipelines

This article explores handling corrected data for the same business date in a Spark pipeline using Iceberg tables. It compares options like skipping, appending, or overwriting partitions, choosing partition overwrite to replace only the affected date's data. The implementation uses DataFrameWriterV2.overwritePartitions() to avoid accidental full-table overwrites.