03-deduplication

To fix the DELTA_MULTIPLE_SOURCE_ROW_MATCHING_TARGET_ROW_IN_MERGE error, deduplicate the source DataFrame on the business key using .orderBy(col("processed_timestamp").desc()).dropDuplicates() before executing the Delta MERGE operation.

Your Delta MERGE fails because the Silver table contains duplicate business keys from incremental DLT streaming or multiple batch loads, violating the single-row match requirement for target updates.

To deduplicate Silver DataFrames before merging into Gold tables, sort the records by the processed_timestamp in descending order and apply dropDuplicates on the business key to retain only the latest version.

Yes, this deduplication pattern handles duplicate records generated by CDC patterns, SCD Type 2 tracking, and multiple batch loads by standardizing the deduplication logic before the Delta MERGE operation.

The best way to prevent duplicate source key errors in Gold layer merges is to implement a standardized deduplication pattern that filters duplicates from the Silver DataFrame before merging data into the Gold dimension table.

Yes, you can use this deduplication pattern for DLT streaming pipelines to resolve duplicate business keys introduced during incremental data ingestion before they cause Gold layer merge conflicts.