Maintaining historical data in dimension tables is important for tracking changes over time and analyzing trends in your data. This can be particularly useful when dealing with dimension tables in data warehousing and business intelligence applications. In this blog post, we will explore some techniques for effectively managing historical data in dimension tables.
1. Slowly Changing Dimensions (SCD)
The most common technique for maintaining historical data in dimension tables is known as Slowly Changing Dimensions (SCD). It provides a mechanism for tracking changes in dimension attributes over time and preserving historical values.
There are three types of SCD:
Type 1 SCD
Type 1 SCD overwrites the existing dimension attribute value with the new one without preserving historical information. This approach is suitable when historical data is not relevant or there is no need to track changes.
Example: Suppose you have a dimension table for “Customers” with attributes like name, age, and address. If a customer changes their address, Type 1 SCD would simply update the address without keeping a record of the old address.
Type 2 SCD
Type 2 SCD creates a new record for each change in dimension attribute values, effectively preserving historical information. This is achieved by adding additional columns in the dimension table, such as a start date and end date, to indicate the validity of the attribute values.
Example: Continuing with the “Customers” dimension table, if a customer’s age changes, Type 2 SCD would create a new record with the updated age and appropriate start and end date values. This allows for tracking changes and analyzing data at different points in time.
Type 3 SCD
Type 3 SCD maintains both the current and previous attribute values in separate columns within the dimension table. This technique sacrifices granularity and can introduce data redundancy, but it can be useful for scenarios where only limited historical data is needed.
Example: For the “Customers” dimension table, a Type 3 SCD approach would involve adding columns like “previous address” and “current address”. When a customer changes their address, the previous address is preserved in the respective column, while the current address is updated.
2. Change Data Capture (CDC)
Another technique for maintaining historical data in dimension tables is Change Data Capture (CDC). CDC captures and stores the changes that occur on the source system, allowing for easy identification and extraction of historical data.
CDC works by leveraging database logs or triggers to identify changes made to the dimension table and then recording those changes in a separate history or audit table.
Example: Let’s say you have a dimension table for “Products” with attributes like name, price, and description. With CDC, any changes made to these attributes in the source system are captured and stored in a separate history table. This history table can then be used to analyze changes over time.
Conclusion
Maintaining historical data in dimension tables is essential for tracking changes and analyzing trends in your data. The techniques discussed in this blog post, such as Slowly Changing Dimensions (SCD) and Change Data Capture (CDC), provide effective methods for managing historical data in dimension tables. By implementing these techniques, you can gain valuable insights and make well-informed decisions based on the historical context of your data.
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