Before considering implementation of OLAP in your data warehouse, you have to take into account two key issues with regard to the MOLAP model running under MDDBMS. The first issue relates to the lack of standardization. Each vendor tool has its own client interface. Another issue is scalability. OLAP is generally good for handling summary data, but not good for volumes of detailed data.

On the other hand, highly normalized data in the data warehouse can give rise to processing overhead when you are performing complex analysis. You may reduce this by using a STAR schema multidimensional design. In fact, for some ROLAP tools, the multidimensional representation of data in a STAR schema arrangement is a prerequisite.

Consider a few choices of architecture. Look at Figure 15-20 showing four architectural options.

You have now studied the various implementation options for providing OLAP functionality in your data warehouse. These are important choices. Remember, without OLAP, your users have very limited means for analyzing data. Let us now examine some specific design considerations.

15.5.1. Data Design and Preparation

The data warehouse feeds data to the OLAP system. In the MOLAP model, separate proprietary multidimensional databases store the data fed from the data warehouse in the form of multidimensional cubes. On the other hand, in the ROLAP model, although no static intermediary data repository exists, data is still pushed into the ...

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