tailieunhanh - SAS Data Integration Studio 3.3- P42

SAS Data Integration Studio P42:This manual is a companion to the online Help for SAS Data Integration Studio. The Help describes all windows in SAS Data Integration Studio, and it summarizes the main tasks that you can perform with the software. The Help includes examples for all source designer wizards, all target designer wizards, and all transformation templates in the Process Library tree. | 200 About the SCD Type 2 Loader Transformation A Chapter 12 the future. As more data points are added the End Date can be changed to a termination date and a new record for the current value can be entered. It is good practice to name the change tracking columns consistently across dimensions in the data model so that they are easily recognizable. In the previous display the column names VALID_FROM_DTTM and VALID_TO_DTTM are used. After a time date range has been specified you can find the desired value when querying the dimension table. Instead of just looking for a specific value in a column of data you have the ability to perform a query for current and or historical data. For example a query could be performed to return the latest address for a household. Because the table has stored date information in the Beginning Date and End Date you can determine the value for this or any other data. Selecting Columns for Change Detection To optimize the loading of the dimension table change detection should be enabled only for those columns that are relevant to the analysis that you will perform. For example the following display shows the Detect Changes tab in the properties window for the SCD Type 2 Loader. Display Detect Changes Tab In the previous display change detection will be performed only for the dimension table columns in the Selected columns pane on the right. The columns that remain in the Available columns pane on the left are apparently not relevant to the analysis that will be performed on the dimension table. The actual change detection is done by a checksum-like algorithm that computes a distinct value based on all the selected input columns. These checksum-like values are compared to detect change. This methodology is fast because it does not require large text-based or column by column compares to detect changes against each row which quickly gets prohibitively expensive as data volumes rise. It is possible to persist the checksum table to a .