Build a watermark-based incremental load in Azure Data Factory, then close its two silent gaps: transactions that commit late and deleted rows, with overlap windows, rowversion and Change Tracking.
Build a metadata-driven Azure Data Factory pipeline: a control table in Azure SQL, Lookup and ForEach fan-out, a parameterised Copy activity landing Parquet in ADLS Gen2, and per-entity run logging.