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Saturday, 11 March 2017


Bottom Up Approach in Data Warehouse


By on March 11, 2017


                                         Bottom-Up Approach






In a bottom-up approach, the goal is to deliver business value by deploying dimensional data marts as quickly as possible. Unlike the top-down approach, these data marts contain all the data—both atomic and summary—that users may want or need, now or in the future. Data is modeled in a star schema design to optimize usability and query performance. Each data mart builds on the next, reusing dimensions and facts so users can query across data marts, if desired, to obtain a single version of the truth as well as both summary and atomic data.


The “bottom-up” approach consciously tries to minimize back-office operations, preferring to focus an organization’s effort on developing dimensional designs that meet end-user requirements. The “bottom-up” staging area is non-persistent, and may simply stream flat files from source systems to data marts using the file transfer protocol. In most cases, dimensional data marts are logically stored within a single database. This approach minimizes data redundancy and makes it easier to extend existing dimensional models to accommodate new subject areas.







This approach is similar to the top-down approach but the emphasis is on the data rather than the business benefit. This is a “proof of concept” type of approach, therefore appealing to IT.

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.”

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