A number of OLTP systems store dimension data in SCD2-like tables in order to retain all the revisions whenever the dimension information changes. In certain situations, you may come across a need to join two or more SCD tables, while keeping all the versions information intact. Sound tricky? Not really.
In datawarehousing, a common scenario you may have encountered is the phenomenon of late-arriving dimension members. Imagine a situation where you get dimension members and fact data from two entirely different data sources, which may or may not provide data at the same time. Some fact data will probably be ready to load before its corresponding dimension member(s) arrive. How do you tag your fact data, and what consequence does that have for the integrity of your data warehouse?
In the third installment of the series on slowly changing dimensions, we’re going to tackle the question of how to manage accumulated fact aggregates in a solution that uses SCD 2 dimensions. While SCD 2 dimensions solve a lot of problems with slowly changing dimensions, accumulated values can still make a mess of the aggregate data.
This is the first article in a series that will describe what slowly changing dimensions (SCD for short) are, how they work, and why you might need to take them into account in your database or datawarehouse solution.