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29-Mar-2020 02:23

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.

The top-down approach views the data warehouse as the linchpin of the entire analytic environment.

The data warehouse holds atomic or transaction data that is extracted from one or more source systems and integrated within a normalized, enterprise data model.

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 (see chart.) 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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