Downstream consumers require incremental changes from a Delta table that includes deletes and updates. You must deliver ordered change data capture (CDC) from version checkpoints. Which Delta feature should you use?

Delta Lake’s Change Data Feed (CDF) is designed for exactly this scenario. CDF records every insert, update, delete, and merge operation in the transaction log and exposes them as a structured stream. By calling spark.read.format("delta").option("readChangeFeed", "true").option("startingVersion", <v>).load(<path>) you can read changes from a specific version or timestamp, guaranteeing that the output is ordered by the commit sequence. CDF preserves the order of operations, so downstream consumers see a true CDC stream. It also handles schema evolution and compaction automatically, so you don’t need to manually replay the log or merge staging tables. CDF is the recommended feature for ordered CDC from Delta tables with deletes and updates.