SCD Type 1 in Databricks refers to a method of handling slowly changing dimensions in data warehousing. Slowly changing dimensions are attributes in a dimension table that change over time, such as a customer’s address or an employee’s job title.

In SCD Type 1, the old record is directly overwritten with the new record when a change occurs. This means that no history of the previous values is retained. This approach is suitable for scenarios where historical tracking is not required and the focus is on maintaining the most current information.

How SCD Type 1 is implemented in Databricks:

Databricks, particularly with Delta Lake, provides various ways to implement SCD Type 1:

• MERGE operation: This is the most common and efficient way. It allows you to match incoming data with existing records based on a key and then either update or insert new records as needed.
• UPDATE statement: You can directly update the existing record with the new values.
• DELETE and INSERT combination: Delete the old record and then insert the new record. This is less efficient than MERGE.

Example (using MERGE):

MERGE INTO target_table
USING source_table
ON target_table.key = source_table.key
WHEN MATCHED THEN
  UPDATE SET
    target_table.column1 = source_table.column1,
    target_table.column2 = source_table.column2
WHEN NOT MATCHED THEN
  INSERT (key, column1, column2)
  VALUES (source_table.key, source_table.column1, source_table.column2)

Considerations:

• No history: SCD Type 1 does not maintain history, which may be a limitation in some use cases.
• Data loss: Since old data is overwritten, there is a risk of losing valuable information.
• Accuracy: Ensure the source data is accurate, as incorrect updates will overwrite the correct data.

Alternatives:

If historical tracking is important, consider using SCD Type 2, which creates a new record for each change, preserving the history of the attribute’s values.