Building the Data Warehouse

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Figure 1.13 shows that the operational environment is supported by the classical systems development life cycle (the SDLC). The SDLC is often called the “waterfall” development approach because the different activities are specified and one activity-upon its completion-spills down into the next activity and triggers its start.

The development of the data warehouse operates under a very different life cycle, sometimes called the CLDS (the reverse of the SDLC). The classical SDLC is driven by requirements. In order to build systems, you must first understand the requirements. Then you go into stages of design and development. The CLDS is almost exactly the reverse: The CLDS starts with data. Once the data is in hand, it is integrated and then tested to see what bias there is to the data, if any. Programs are then written against the data. The results of the programs are analyzed, and finally the requirements of the system are understood. The CLDS is usually called a “spiral” development methodology. A spiral development methodology is included on the Web site,




classical SDLC

•    requirements gathering

•    analysis

•    design

•    programming

•    testing

•    integration

•    implementation data warehouse SDLC

•    implement warehouse

•    integrate data

•    test for bias

•    program against data

•    design DSS system

•    analyze results

•    understand requirements

Figure 1.13 The system development life cycle for the data warehouse environment is almost exactly the opposite of the classical SDLC.

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