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Saturday, 11 March 2017


Top down approach in Data Warehouse


By on March 11, 2017

Top-Down Approach

Also referred as “Big-Bang” approach.

There are certain similarities and differences among these 4 data warehouse approaches. This is especially true of the “top-down” and “bottom-up” approaches. The “top-down” and “bottom-up” approaches have existed the longest and occupy the polar ends of the development spectrum.

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.


Top down approach in Data Warehouse
Top down approach in Data Warehouse


In the top-down approach, 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. From there, the data is summarized, dimensional, and distributed to one or more “dependent” data marts. These data marts are “dependent” because they derive all their data from a centralized data warehouse.

Sometimes, organizations supplement the data warehouse with a staging area to collect and store source system data before it can be moved and integrated within the data warehouse. A separate staging area is particularly useful if there are numerous source systems, large volumes of data, or small batch windows with which to extract data from source systems.

The major benefit of a “top-down” approach is that it provides an integrated, flexible architecture to support downstream analytic data structures. First, this means the data warehouse provides a departure point for all data marts, enforcing consistency and standardization so that organizations can achieve a single version of the truth.

Second, the atomic data in the warehouse lets organizations re-purpose that data in any number of ways to meet new and unexpected business needs. For example, a data warehouse can be used to create rich data sets for statisticians, deliver operational reports, or support operational data stores (ODS) and analytic applications. Moreover, users can query the data warehouse if they need cross-functional or enterprise views of the data.

On the downside, a top-down approach may take longer and cost more to deploy than other approaches, especially in the initial increments. This is because organizations must create a reasonably detailed enterprise data model as well as the physical infrastructure to house the staging area, data warehouse, and the marts before deploying their applications or reports. (Of course, depending on the size of an implementation, organizations can deploy all three “tiers” within a single database.) This initial delay may cause some groups with their own IT budgets to build their own analytic applications. Also, it may not be intuitive or seamless for end users to drill through from a data mart to a data warehouse to find the details behind the summary data in their reports.


Top down approach in Data Warehouse
Top down approach in Data Warehouse



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