Slowly Changing Dimension (SCD) Type 2 is a common data warehousing technique to track changes in dimension attributes over time. Databricks, with its unified analytics platform and Delta Lake, offers efficient ways to implement SCD Type 2.

Understanding SCD Type 2

SCD Type 2 preserves the full history of changes by:

• Creating a new row: Whenever a dimension attribute changes, a new row is inserted into the dimension table.
• Start and End Dates: Each row has a start_date (when the record became valid) and an end_date (when it ceased to be valid). The current record has an end_date of null.
• Surrogate Key: A unique identifier (often an auto-incrementing integer) is used as the primary key to distinguish between different versions of the same dimension record.

Implementing SCD Type 2 in Databricks

There are several ways to implement SCD Type 2 in Databricks:

1. Delta Lake and MERGE:

   • Delta Lake’s MERGE statement is a powerful tool for implementing SCD Type 2. It allows you to insert, update, and delete rows in a single operation based on matching conditions.
   SQL

   MERGE INTO dimension_table AS target
   USING updates_table AS source
   ON target.business_key = source.business_key
   WHEN MATCHED AND target.attribute <> source.attribute THEN
     UPDATE SET target.end_date = current_date
   WHEN NOT MATCHED THEN
     INSERT (business_key, attribute, start_date, end_date) 
     VALUES (source.business_key, source.attribute, current_date, null)
   

2. Databricks SQL (DBSQL) and APPLY CHANGES:

   • DBSQL and APPLY CHANGES streamline change data capture (CDC) operations making it easy to maintain SCD Type 2 tables.
   SQL

   APPLY CHANGES INTO dim_products
   FROM product_cdc
   KEYS (product_id)
   APPLY AS DELETE WHEN is_deleted = true
   APPLY AS UPDATE SET ...
   APPLY AS INSERT VALUES ...
   

3. PySpark:

   • You can also use PySpark to implement SCD Type 2 logic by comparing incoming data with the existing dimension table and performing the necessary inserts and updates.