SCD Type 3 in Databricks is a method for managing slowly changing dimensions within a data warehouse. It focuses explicitly on tracking an attribute’s current and previous values when it changes. This approach allows you to maintain a history of changes while still having access to the most recent data.

Implementation Approaches:

While Databricks doesn’t have a built-in SCD Type 3 implementation, you can effectively implement it using the following methods:

1. Delta Live Tables (DLT):
   • Leverage the APPLY CHANGES API within DLT to simplify change data capture.
   • Use MERGE statements to efficiently update your dimension table efficiently, preserving old and new attribute values.
   • Handle out-of-order events and ensure data consistency.
2. Spark Structured Streaming:
   • Process incoming data streams in real time.
   • Apply similar MERGE logic to maintain the SCD Type 3 structure.
   • Ensure fault tolerance and exact-once processing for reliable updates.