What is the general purpose of slowly changing dimensions?

What is the general purpose of slowly changing dimensions?

• A. Do not store any history
• B. Keep only the latest value, overwriting history
• C. Create multiple independent history tables
• D. Keep track of the change in dimension values and report historical data at any given point in time

Answer: (D)

Slowly changing dimensions are about preserving how a dimension’s attributes change over time so you can analyze data as it existed at any point in history. The general purpose is to keep track of the changes in dimension values and be able to report historical data for a specific time, by reconstructing the state of the dimension as of that moment. In practice, you version the dimension (for example, adding a new row when a value changes and recording validity dates or a surrogate key) so queries can answer questions like “what was the customer’s attribute value on this date?”. Not storing history loses past context; overwriting history erases how values looked before. Creating multiple independent history tables adds unnecessary complexity and makes it harder to consistently join back to the main fact data. The essence of slow changing dimensions is maintaining a historical record of changes to enable accurate time-based analysis.

Slowly changing dimensions are about preserving how a dimension’s attributes change over time so you can analyze data as it existed at any point in history. The general purpose is to keep track of the changes in dimension values and be able to report historical data for a specific time, by reconstructing the state of the dimension as of that moment. In practice, you version the dimension (for example, adding a new row when a value changes and recording validity dates or a surrogate key) so queries can answer questions like “what was the customer’s attribute value on this date?”.

Not storing history loses past context; overwriting history erases how values looked before. Creating multiple independent history tables adds unnecessary complexity and makes it harder to consistently join back to the main fact data. The essence of slow changing dimensions is maintaining a historical record of changes to enable accurate time-based analysis.