Building the Data Warehouse

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■■ Methodologies are often sold as solutions, not tools. When a methodology is sold as a solution, inevitably it is asked to replace good judgment and common sense, and this is always a mistake.


■ Methodologies often generate a lot of paper and very little design. Design and development activities are not legitimately replaced by paper.


Methodologies can be very complex, anticipating every possibility that may ever happen. Despite these drawbacks, there still is some general appeal for methodologies. A general-purpose methodology—applicable to the data-driven environment—is described in the appendix, with full recognition of the pitfalls and track record of methodologies. The data-driven methodology that is outlined owes much to its early predecessors. As such, for a much fuller explanation of the intricacies and techniques described in the methodology, refer to the books listed in the references in the back of this book.


One of the salient aspects of a data-driven methodology is that it builds on previous efforts—utilizing both code and processes that have already been developed. The only way that development on previous efforts can be achieved is through the recognition of commonality. Before the developer strikes the first line of code or designs the first database, he or she needs to know what already exists and how it affects the development process. A conscious effort must be made to use what is already in place and not reinvent the wheel. That is one of the essences of data-driven development.


The data warehouse environment is built under what is best termed an iterative development approach. In this approach a small part of the system is built to completion, then another small part is completed, and so forth. That development proceeds down the same path repeatedly makes the approach appear to be constantly recycling itself. The constant recycling leads to the term “spiral” development.

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