Taking AUTO CDC to the Next Level: Solving the Hardest Real-World Use Cases
Databricks, Tuesday, August 11th, 2026
Databricks extends AUTO CDC with bitemporal support and partial updates, contributing to Apache Spark 4.2.
Databricks detailed new capabilities in AUTO CDC, its change data capture feature, adding bitemporal support and partial record updates. Change data capture is one of the most common things data engineers build on Spark and one of the most tedious to get right.
The additions target compliance scenarios requiring bitemporal history and cases where only part of a record changes, delivering audit-ready CDC without custom code.
Some of the work has been contributed upstream to Apache Spark 4.2. The post is authored by Josh Seidel, Shanelle Roman and Sudhanva Huruli.