To those who are unfamiliar with Ralph Kimball and Bill Inmon data warehouse architectures please read the following articles: Ralph Kimball dimensional data . Summary: in this article, we will discuss Bill Inmon data warehouse architecture which is known as Corporate Information Factory. Bill Inmon, the “Father of Data Warehousing,” defines a Data Warehouse (DW) as , “a subject-oriented, integrated, time-variant and non-volatile collection of data.

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This article attempts to draw out the similarities and differences between the Inmon and Kimball approaches to the data warehouse.

A Short History of Data Warehousing – DATAVERSITY

On the subject of what the data warehouse is and what the data marts are, both Kimball and Inmon have spoken:. Accordingly, the two architectures have some elements in common. Corporations must develop operating and feedback systems to use the underlying data means the data warehouse to achieve their goals. Another requirement of any data warehouse architecture is that the user can depend on the accuracy and timeliness of the data. The user must also be able to access the data according to his or her particular needs through an easily understandable and straightforward manner of making queries.

The data that is extracted in this manner by one user should be compatible with and translatable to other operations and users within the same group or enterprise that rely on the same data. Both Inmon and Kimball share the opinion that stand-alone or independent data marts or data warehouses do not satisfy the needs for accurate and timely data and ease of access for users on an enterprise or corporate scale.

But over time there are never only a few data marts Once there are … a lot of data marts, the independent data mart approach starts to fall apart.


Kimball gives his opinion of independent data marts:. These independent silos are built to satisfy specific needs, without regard to other existing or planned analytic data. They tend to be departmental in nature, often loosely dimensionally structured. Although often perceived as the path of least resistance because no coordination is required, the independent approach is unsustainable in the long run. Multiple, uncoordinated extracts from the same operational sources are inefficient and wasteful.

They generate similar, but different variations with inconsistent naming conventions and business rules. The conflicting results cause confusion, rework and reconciliation.

In the end, decision-making based on independent data is often clouded warheouse fear, uncertainty and doubt. It appears from the above, that both Inmon and Kimball are of the opinion that independent or stand-alone data marts are of marginal use. However, for the most part, this is warehoyse the perception of similarity stops. You may discern later, as I have, that there are more similarities, but each of our data warehouse architects expresses them in a very different way.

Bill Inmon Data Warehouse

When there is an enterprise need for data the star schema is not at all optimal. Taken together, a series of star schemas and multi-dimensional tables are brittle Inmon believes his approach, which uses the dependent data mart as the source for star schema usage, solves the problem of enterprise-wide access to the same data, which can change over time.

Staging begins with coordinated extracts from the operational source systems. The above indicates to this author that Kimball has gone beyond the individual star schema approach, criticized by Inmon and, in fact, has described his multi-dimensional data warehouse.

In this approach, the model contains atomic data and the summarized data, but its construction is based on business measurements, which enable disparate business departments to query the data from a higher level of detail to the lowest level without reprogramming.


In Mastering Data Warehouse Design: Relational and Dimensional Techniquesby Claudia Imhoff, Nicholas Galemmo and Jonathan Geiger Wiley,these authors analyze the Kimball approach as relying on star schemas for both atomic and aggregated storage.

Summarizing this point of their research, the Data Warehouse Bus Architecture is said to consist of two types of data marts:. Their description of the Kimball Bus Architecture seems to indicate that the Kimball Approach still does not recognize a need for nor require a central data warehouse repository.

The next article will highlight the differences in the two models regarding relational vs. She was responsible for the content review, editing and formatting of international newsletters focusing on business intelligence and data warehousing.

Bill Inmon – Wikipedia

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