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Answer: Type 1 – No History. Update record directly, there is no record of historical values, only current state., Type 2 – Row Versioning. Involves adding new rows to the dimension table when changes occur. It allows us to track historical versions of attributes.
For the Customers table, use Type 1 SCD (No History) if you only need to maintain the current state without keeping historical data. This type overwrites existing data with new values. For the Products table, use Type 2 SCD (Row Versioning) to track historical versions of attributes. This involves adding new rows to the dimension table when changes occur, allowing you to maintain a history of changes.
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As a Fabric Analytics Engineer, you are responsible for implementing two dimension tables named Customers and Products in a Fabric data warehouse. To manage the versioning of data effectively, you must use a Slowly Changing Dimension (SCD) approach. Ensure that the solution adheres to the requirements specified in the table below. Determine the most appropriate type of SCD to use for each table based on these requirements.
Static table: Type 0 – Fixed Dimension. No changes allowed, dimension never changes.
Commonly used SCD types: Type 1 – No History. Update record directly, there is no record of historical values, only current state. Type 2 – Row Versioning. Type 3 – Previous Value column.
Rarely used: Type 4 – History Table. Type 6 – Hybrid SCD.
Which type of SCD should you implement for each of the dimension tables?
A
Type 0 – Fixed Dimension. No changes allowed, dimension never changes.
B
Type 1 – No History. Update record directly, there is no record of historical values, only current state.
C
Type 2 – Row Versioning. Involves adding new rows to the dimension table when changes occur. It allows us to track historical versions of attributes.
D
Type 3 – Previous Value column. Column to store previous values.