Building the Data Warehouse

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■    The basic DBMS decision must be revisited from time to time.

Should the decision be made to go to a new DBMS technology, what are the considerations? A few of the more important ones follow:

■    Will the new DBMS technology meet the foreseeable requirements?

■    How will the conversion from the older DBMS technology to the newer DBMS technology be done?

■    How will the transformation programs be converted?

Of all of these considerations, the last is the most vexing. Trying to change the transformation programs is a complex task in the best of circumstances.

The fact remains that once a DBMS has been implemented for a data warehouse, change at a later point in time is a possibility. Such was never the case in the world of transaction processing; once a DBMS had been implemented, that DBMS stayed as long as the transactions were being run.

Multidimensional DBMS and the Data Warehouse

One of the technologies often discussed in the context of the data warehouse is multidimensional database management systems processing (sometimes called OLAP processing). Multidimensional database management systems, or data marts, provide an information system with the structure that allows an organization to have very flexible access to data, to slice and dice data any number of ways, and to dynamically explore the relationship between summary and detail data. Multidimensional DBMSs offer both flexibility and control to the end user, and as such they fit well in a DSS environment. A very interesting and complementary relationship exists between multidimensional DBMSs and the data warehouse, as shown in Figure 5.5.

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