The "Slowly Changing Dimension" problem is a common one particular to data warehousing. In a nutshell, this applies to cases where the attribute for a record varies over time. We give an example below:
Christina is a customer with ABC Inc. She first lived in Chicago, Illinois. So, the original entry in the customer lookup table has the following record:
At a later date, she moved to Los Angeles, California on January, 2003. How should ABC Inc. now modify its customer table to reflect this change? This is the "Slowly Changing Dimension" problem.
There are in general three ways to solve this type of problem, and they are categorized as follows:
Type 1: The new record replaces the original record. No trace of the old
Type 2: A new record is added into the customer dimension table.
Therefore, the customer is treated essentially as two people.
Type 3: The original record is modified to reflect the change.
We next take a look at each of the scenarios and how the data model and the data looks like for each of them. Finally, we compare and contrast among the three alternatives.