Kimball vs. Inmon Data Warehouse Architectures

Summary: in this article, we will discuss the differences between Kimball and Inmon in data warehouse architecture approach.

To those who are unfamiliar with Ralph Kimball and Bill Inmon data warehouse architectures please read the following articles:

Kimball vs Inmon in data warehouse architecture

Both Kimball and Inmon’s architectures share a same common feature that each has a single integrated repository of atomic data. In Inmon’s architecture, it is called enterprise data warehouse. And in Kimball’s architecture, it is known as the dimensional data warehouse. Both architectures have an enterprise focus that supports information analysis across the organization. This approach enables to address the business requirements not only within a subject area but also across subject areas.

However, there are some differences in the data warehouse architectures of both experts:

  • Kimball uses the dimensional model such as star schemas or snowflakes to organize the data in dimensional data warehouse while Inmon uses ER model in enterprise data warehouse. Inmon only uses dimensional model for data marts only while Kimball uses it for all data
  • Inmon uses data marts as physical separation from enterprise data warehouse and they are built for departmental uses. While in Kimball’s architecture, it is unnecessary to separate the data marts from the dimensional data warehouse.
  • In dimensional data warehouse of Kimball, analytic systems can access data directly. While in Inmon’s architecture, analytic systems can only access data in enterprise data warehouse via data marts.

Kimball vs. Inmon in data warehouse building approach

Kimball vs Inmon

Bill Inmon

Bill Inmon recommends building the data warehouse that follows the top-down approach. In Inmon’s philosophy, it is starting with building a big centralized enterprise data warehouse where all available data from transaction systems are consolidated into a subject-oriented, integrated, time-variant and non-volatile collection of data that supports decision making. then data marts are built for analytic needs of departments.

Kimball vs Inmon

Ralph Kimball

Contrast to Bill Inmon approach, Ralph Kimball recommends building the data warehouse that follows the bottom-up approach. In Kimball’s philosophy, it first starts with mission-critical data marts that serve analytic needs of departments. Then it is integrating these data marts for data consistency through a so-called information bus. Kimball makes uses of the dimensional model to address the needs of departments in various areas within the enterprise.

How to choose between Kimball vs Inmon approach for building data warehouse?

Here are the most important criteria how to choose between Kimball vs Inmon approach.

CharacteristicsFavours KimballFavours Inmon
Business decision support requirementsTacticalStrategic
Data integration requirementsIndividual business requirementsEnterprise-wide integration
The structure of dataKPI, business performance measures, scorecards…Data that meet multiple and varied information needs and non-metric data
Persistence of data in source systemsSource systems are quite stableSource systems have high rate of change
Skill setsSmall team of generalistsBigger team of specialists
Time constraintUrgent needs for the first data warehouseLonger time is allowed to meet business’ needs.
Cost to buildLow start-up costHigh start-up costs

In this article, we’ve discussed the Kimball vs Inmon in data warehouse architecture and design approach. In addition, we’ve provided the information that you can choose between Kimball vs Inmon to build your data warehouse.

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