![]() ![]() Usually, dimensions containing Type 2 history have effective and expiration dates, as well as a current indicator, which must be maintained as Type 2 SCD rows are inserted. Historical fact rows are linked through the surrogate key to the version of the dimension row that was current when the fact was recorded. It is made possible by the use of a surrogate key on the dimension rather than the natural key. The workhorse of dimension history is, therefore, SCD Type 2. SCD Type 1 is a simple overwrite, and SCD Type 3 is somewhat special-purpose and limited. For each column in the dimension table, a determination should be made to 1) overwrite the old value, 2) insert a new row in the dimension with a new dimension key to record the new value, preserving the old, or 3) copy the old value to a previous value column in the row. Kimball’s general answer is to choose between the standard slowly changing dimension (SCD) Types 1, 2, and 3. When dimensions change, how should it be handled? Not every dimension change needs to be recorded as history, but many do. #TIMEXTENDER HISTORY LOAD CODE#The values of Dimensions are either static (date and time, limited code sets) or change slowly. Warehouse facts are inherently historical since transactions happen on a transaction date, balances are kept on a balance date, and so on. As we will see, clearly distinguishing between current and past dimension values pays off in clarity of design, flexibility of presentation, and ease of ETL maintenance. That said, can the mainstay Type 2 slowly changing dimension be improved? I here present the concept of historical dimensions as a way to solve some issues with the basic Type 2 slowly changing dimension promoted by Kimball. ![]() Kimball’s practical approach focuses squarely on clarity and ease of use for the business users of the warehouse. Business users can understand and query these warehouses directly and gain valuable insights into the business. His practical warehouse design and conformed-dimension bus architecture are the industry standard. We owe a lot to Ralph Kimball and friends. This solution gives you much more flexibility and control over incremental loading between staging and the data warehouse, and also resolves this issue.Clarifying Data Warehouse Design with Historical Dimensions - Simple Talk Skip to content SolutionĪs a best practice, we suggest pulling the staging table's DW_Timestamp field into the data warehouse and using that as the incremental field instead of relying on a source database incremental field. This means that using timestamp as your incremental field will work from your source into staging, but misbehave if used as an incremental field when configuring loads into a data warehouse. ![]() TimeXtender's NAV adapter has special logic that can compare on the NAV timestamp field, but incremental loading requires a date, datetime, or numeric incremental value everywhere else. In most source systems, the time stamp or modified date field for a record is some kind of date/time data type, but NAV uses a varbinary(8) data type instead. This problem happens if you use the NAV "timestamp" field for incremental loading into the data warehouse. #TIMEXTENDER HISTORY LOAD FULL#Data may be missing after a full load, or the incremental load may not work properly. Incremental loading works when importing data from a BC data source into staging, but produces unexpected results when using incremental loading into the data warehouse. ![]()
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