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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.
The new date data types in SQL server crack a problem I have had since I started BI as there is (nearly) always a time dimension in every…
However just because we are measuring performance doesn’t mean our work is done. Read more If you glance to the side of this post you will see that one of the few books on my bookshelf is the Microsoft Data Warehouse Toolkit, and so I am annoyed that I can’t make the course of the book, presented by the authors, Joy Mundy and Warren Thornthwaite. Read more You would expect there to be a record and hopefully only one record for each sales person in a typical sales system and another record in another table for each of the customers.