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Kimball vs Inmon

In the realm of datawarehouse design, there are two major design philosophies, one advocated by Bill Inmon and one by Ralph Kimball.

What are the major differences between these two approaches?

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asked Oct 21, 2009 at 06:05 AM in Default

Kev Riley gravatar image

Kev Riley ♦♦
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In Ralph Kimballs philosophy, Bottom-up design, you first create the datamarts needed for reports and analysis, and then these data marts can eventually be unioned together to create a comprehensive data warehouse.

In Bill Inmons philosophy, Top-down design, you first design a normalized enterprise data model, and then the Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.

The main benefit from Kimball is that Business value can be returned quickly.

Quote from www.wikipedia.com:

Ralph Kimball : Bottom-up design

Ralph Kimball is a proponent of an approach to data warehouse design frequently considered as bottom-up. In the so-called bottom-up approach data marts are first created to provide reporting and analytical capabilities for specific business processes. Data marts contain atomic data and, if necessary, summarized data. These data marts can eventually be unioned together to create a comprehensive data warehouse. The combination of data marts is managed through the implementation of what Kimball calls "a data warehouse bus architecture".

Business value can be returned as quickly as the first data marts can be created. Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent. In Kimball words, this means that the dimensions "conform".

Bill Inmon : Top down design

Bill Inmon, one of the first authors on the subject of data warehousing, has defined a data warehouse as a centralized repository for the entire enterprise.[6] Inmon is one of the leading proponents of the top-down approach to data warehouse design, in which the data warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities.

Inmon states that the data warehouse is:

  • Subject-oriented : The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.
  • Non-volatile : Data in the data warehouse is never over-written or deleted - once committed, the data is static, read-only, and retained for future reporting.
  • Integrated : The data warehouse contains data from most or all of an organization's operational systems and this data is made consistent.

The top-down design methodology generates highly consistent dimensional views of data across data marts since all data marts are loaded from the centralized repository. Top-down design has also proven to be robust against business changes. Generating new dimensional data marts against the data stored in the data warehouse is a relatively simple task. The main disadvantage to the top-down methodology is that it represents a very large project with a very broad scope. The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial. In addition, the top-down methodology can be inflexible and unresponsive to changing departmental needs during the implementation phases

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answered Oct 21, 2009 at 08:20 AM

Håkan Winther gravatar image

Håkan Winther
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asked: Oct 21, 2009 at 06:05 AM

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Last Updated: Oct 21, 2009 at 06:05 AM