Data Warehousing: Kimball vs Inmon
Anyone involved in the Business Intelligence space has had their head in the sand if they are not aware of the long running, and more often than not misunderstood, debate between the two conceptual models of data warehouse design.
Bill Inmon has recently posted an article on www.b-eye-network.com discussing the matter, and to his credit, has tried to put forward a number of balanced pros and cons of each methodology.
I’ll state now that I’m a big advocate of a hybrid approach, taking elements from both Imnon and Kimball camps and selecting the right approach for each unique project depending on the requirements and purpose of the warehouse. I therefore appreciate both sides of the debate, and am not going to jump to the defence of either side. Having said that, most projects more often than not have a weighting towards Kimball due to the time pressures imposed by clients.
Bill nicely simmarises the key elements of each approach as:
The Kimball approach to database design and development is typified by the star schema design of databases. There are fact tables and dimension tables. In a complex environment, there are snowflake structures, which are merely extended versions of the star schema. In order to resolve differences of granularity between fact tables, conformed dimensions are used. Staging areas are occasionally used to capture raw data before the placement of the data into a Kimball style data mart.
The Inmon approach to data warehousing centers around a relational, non redundant, granular, integrated design of detailed data. From this base of data, data marts are spun off to different departments according to their individual analytical needs. In recent vintage, with DW 2.0, the Inmon approach calls for the recognition of the life cycle of data within the data warehouse, the inclusion and integration of unstructured data within the data warehouse, and the close integration of metadata into the data warehouse infrastructure.
He then proceeds to present a brief comparitive assessment of the pros and cons of each. I don’t entirely agree with the black and white nature of the comparisons as most items are a shade of grey in both camps, but it certainly provides a good starting point for those that are starting out in the BI field and want to know more about whhat this debate is alll about.
Possibly the most interesting item (certainly from a business intelligence consultant’s perspective) is Bill’s renewed call for an open, public debate between Bill and Ralph – It gets my vote!
Thanks to Graham Bradfield at Computer People for pointing me towards this article in his BI newsletter.
View the full article here: Data Warehousing: Kimball vs Inmon