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Thursday, 3 March 2016

Data Warehouse Approaches #1



It has been said there are as many ways to build data warehouses as there are companies to build them. Each data warehouse is unique because it must adapt to the needs of business users in different functional areas, whose companies face different business conditions and competitive pressures.
Nonetheless, three major approaches to building a data warehousing environment exist. These approaches are generally referred to as:

1. Top-down
2. Bottom-up
3. Hybrid

Although we have been building data warehouses since the early 1990s, there is still a great deal of confusion about the similarities and differences among these architectures. This is especially true of the "top-down" and "bottom-up" approaches, which have existed the longest and occupy the polar ends of the development spectrum.
As a result, some organizations fail to adopt a clear vision for the way the data warehousing environment can and should evolve. Others, paralysed by confusion or fear of deviating from prescribed tenets for success, cling too rigidly to one approach or another, undermining their ability to respond flexibly to new or unexpected situations. Ideally, organizations need to borrow concepts and tactics from each approach to create environments that uniquely meets their needs.



Semantic and Substantive Differences The two most influential approaches are championed by industry heavyweights Bill Inmon and Ralph Kimball, both prolific authors and consultants in the data warehousing field.
Inmon, who is credited with coining the term "data warehousing" in the early 1990s, advocates a top-down approach, in which companies first build a data warehouse followed by data marts.
Kimball’s approach, on the other hand, is often called bottom-up because it starts and ends with data marts, negating the need for a physical data warehouse altogether.

On the surface, there is considerable friction between top-down and bottom-up approaches. But in reality, the differences are not as stark as they may appear. Both approaches advocate building a robust enterprise architecture that adapts easily to changing business needs and delivers a single version of the truth. In some cases, the differences are more semantic than substantive in nature. For example, both approaches collect data from source systems into a single data store, from which data marts are populated. But while "top-down" subscribers call this a data warehouse, "bottom-up" adherents often call this a "staging area."

Nonetheless, significant differences exist between the two approaches, Data warehousing professionals need to understand the substantial, subtle, and semantic differences among the approaches and which industry "gurus" or consultants advocate each approach. This will provide a clearer understanding of the different routes to achieve data warehousing success and how to translate between the advice and rhetoric of the different approaches.





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