Data warehousing and business intelligence are crucial for modern enterprises, enabling them to make informed decisions based on historical data. One key aspect of data warehousing is handling changing data over time, especially when tracking historical changes in dimensions. This is where Slowly Changing Dimensions (SCD) come into play. In this blog post, we’ll delve into what SCDs are, the different types of SCDs, and how to implement them effectively.
What are Slowly Changing Dimensions (SCD)?
Slowly Changing Dimensions (SCD) refer to dimensions in a data warehouse that change slowly over time, rather than changing on a regular schedule, daily or weekly. Managing these changes is critical for maintaining accurate historical data. SCDs help track historical changes without losing the context of past data, providing a more comprehensive view of business processes over time.
Types of Slowly Changing Dimensions
There are multiple kinds of slowly changing dimension, of which three are commonly implemented, each addressing different requirements for tracking historical changes:
Type 0: Retain Original
Type 0 SCDs involve keeping the original data unchanged, meaning no updates are made to the data once it is inserted. This method is typically used for attributes that should never change over time, preserving the original values.
Use Case:
- Retaining static attributes
- Ensuring certain data remains unchanged for auditing or historical accuracy
Implementation:
- No updates are made to the existing data.
- The original values are preserved indefinitely.
Example: Storing the original date of birth of a customer, which should not change over time.
Type 1: Overwrite
Type 1 SCDs involve simply overwriting the existing data with new data. This method does not track historical changes, and only the latest information is stored in the data warehouse.
Use Case:
- Correcting data errors
- Updating non-essential attributes where historical data is not required
Implementation:
- Update the existing record with the new data.
- No need to maintain historical data.
Example: Updating a store name without keeping the old name.
| StoreKey | StoreAltKey | StoreName |
|---|---|---|
| 123 | EH199J |
Type 2: Add New Row
Type 2 SCDs involve creating a new row in the dimension table for each change, preserving the historical data. Each row has a unique identifier and associated timestamps (start and end dates) to track the validity of the data over time.
Use Case:
- Tracking historical changes in key attributes
- Maintaining a history of changes for audit purposes
Implementation:
- Add a new row with the updated information.
- Include start and end dates to track the validity of each record.
- Optionally, add a current flag to indicate the active record.
Example: When a customer moves to a new address, add a new row with the updated address and the date the change took effect.
| USERKey | USERAltKey | Name | Address | City | DateFrom | DateTo | IsCurrent |
|---|---|---|---|---|---|---|---|
| 1211 | jo@contoso.com | Jo Smith | 999 Main St | Seattle | 20190101 | 20230105 | False |
| 2996 | jo@contoso.com | Jo Smith | 1234 9th Ave | Boston | 20230106 | True |
Implementing Slowly Changing Dimensions
Step 1: Identify the Attributes
Identify the attributes in your dimension tables that require tracking changes. Determine the type of SCD that best fits your business requirements for each attribute.
Step 2: Design the Dimension Table
Design your dimension table based on the chosen SCD type. Ensure that the table structure supports tracking changes as required. For Type 2 SCDs, include additional columns for start and end dates, and a current flag if needed. For Type 3 SCDs, add columns for previous values.
Step 3: Implement ETL Processes
Implement Extract, Transform, Load (ETL) processes to handle the changes in dimension data:
- Extract: Extract data from source systems, identifying changes in dimension attributes.
- Transform: Transform the data to fit the structure of your dimension table, applying the logic for handling SCDs.
- Load: Load the transformed data into the dimension table, updating existing records or adding new rows/columns as necessary.
Step 4: Test and Validate
Thoroughly test and validate your SCD implementation to ensure that historical changes are tracked accurately and that the data remains consistent. Verify that the ETL processes correctly handle changes according to the chosen SCD type.
Example: Logic to implement Type 1 and Type 2 updates can be complex, and there are various techniques you can use. For example, you could use a combination of UPDATE and INSERT statements.
-- New Customers
INSERT INTO dbo.DimCustomer
SELECT stg.*
FROM dbo.StageCustomers AS stg
WHERE NOT EXISTS
(SELECT * FROM dbo.DimCustomer AS dim
WHERE dim.CustomerAltKey = stg.CustNo)
-- Type 1 updates (name)
UPDATE dbo.DimCustomer
SET CustomerName = stg.CustomerName
FROM dbo.StageCustomers AS stg
WHERE dbo.DimCustomer.CustomerAltKey = stg.CustomerNo;
-- Type 2 updates (StreetAddress)
INSERT INTO dbo.DimCustomer
SELECT stg.*
FROM dbo.StageCustomers AS stg
JOIN dbo.DimCustomer AS dim
ON stg.CustNo = dim.CustomerAltKey
AND stg.StreetAddress <> dim.StreetAddress;Conclusion
Slowly Changing Dimensions are a crucial aspect of data warehousing, allowing businesses to maintain accurate historical data and gain valuable insights from their data. By understanding the different types of SCDs and implementing them effectively, you can ensure that your data warehouse provides a comprehensive view of your business processes over time. Whether you need to retain original data, overwrite data, add new rows, or add new columns, choosing the right SCD type will help you manage changing data efficiently and effectively.