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Professional Microsoft® SQL Server® Analysis Services 2008 with MDX by Denny Guang-Yeu Lee, Robert Zare, Sethu Meenakshisundaram, Matt Carroll, Sivakumar Harinath

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21.3. Long Latency Scenario

Assume you own a small company that is selling a key set of products that is essentially static in nature; the base list of products just doesn't change. New products may be added to the list, but the original set of products remains the same. In this scenario, several of your products are sold each day and the sales data arrives at your data warehouse sometime after normal business hours. Further, your company is headquartered in the United States. The business analysts on your team want to see sales data no later than the next working day following the actual sale. In this scenario, assume incremental processing of your cube takes a relatively small amount of time (just 1–2 hours), which can be completed before start of the next business day. Also, assume that data updates (information about new products added into the system) in your relational databases arrive within a reasonable time.

The traditional approach to solving the outlined scenario would be to have the dimension storage as MOLAP and do an incremental update of dimensions after the relational data update is completed. This approach is computation intensive and is a fairly costly operation. Following the dimension data update, an incremental process of the relevant measure groups is required and, once that completes, the consumers of the cube can browse the results. This approach has advantages. Indeed, this approach is good whenever your relational data updates occur regularly at a specific ...

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