Databricks Change Data Feed CDF Explained | Incremental Data Processing

Databricks Delta Change Data Feed (CDF) Explained | Incremental Processing, Late-Arriving Data & Idempotency What happens when new data arrives late, existing records change, or your Databricks pipeline runs more than once? How do you process only the changes without reprocessing your entire dataset? In Episode 4 of our Databricks Beginner Series, we are taking our existing Bronze → Silver → Gold pipeline to the next level using Delta Change Data Feed (CDF). We will build an incremental data pipeline that tracks changes, handles late-arriving records, and produces consistent results even when processing is repeated. About this series This is a hands-on Databricks series for beginners. We are building and improving the same data engineering project across multiple episodes. Episode 1: Build Your First Databricks Data Pipeline (Bronze, Silver & Gold) Episode 2: Delta Lake OVERWRITE vs APPEND vs MERGE Episode 3: Incremental File Ingestion with Databricks Auto Loader Episode 4: Delta Change Data Feed, Late-Arriving Data & Incremental Processing 👈 YOU ARE HERE Episode 5 (Coming Next): Structured Streaming & foreachBatch Let's discuss! Would you use a custom progress table to track processed Delta versions, or would you prefer Structured Streaming with checkpoints? Let me know in the comments! If you found this tutorial helpful, please LIKE the video, SUBSCRIBE to ThatDataGuySA, and share it with someone learning data engineering. #Databricks #DeltaLake #ChangeDataFeed #PySpark #DataEngineering #IncrementalProcessing #AzureDatabricks #MedallionArchitecture #DataEngineeringTutorial #ThatDataGuySA