Ralph Kimball vs. Bill Inmon approaches to data warehouse design

Learn about the debate between the Ralph Kimball and Bill Inmon approaches to data warehouse design.

What is your opinion in regards to the Ralph Kimball vs. Bill Inmon approaches to data warehouse design?
Ralph Kimball vs. Bill Inmon

I'll define approach as a combination of methodology (build-out schedule) and architecture (data flows and structures)....

Both Bill Inmon and Ralph Kimball have made tremendous contributions to our industry.

I have an entire presentation on this subject, but I'll try to boil it down here. There are fewer differences than people think between the two approaches to data warehouse design. It's mostly a matter of different focuses (Inmon - architecture, Kimball - modeling) and slightly different terminology.

I strongly believe that customization and extension are required for each situation. Neither approach is comprehensive to answer all questions nor remove your need to apply judgment.

You'll have two basic decisions to make and numerous follow-on decisions:

  • Enterprise vs. Data Mart-Oriented Architecture
  • Enterprise vs. Bottoms-up Oriented Methodology

Example follow-on decisions:

  • Build out scope
  • Business involvement
  • Definition of data marts - units of work or physical expansiveness of use with ETL tool in ETL
  • Processes data access options and manner of selection - by use, by enterprise, by category
  • Data retention and archival definition of data marts - units of work or physical expansiveness of use of ETL tool in ETL processes
  • Granularity of data capture integration strategy – virtual and physical metadata handling modeling technique(s) need, utility and physical nature of data marts
  • Operational reporting and monitoring - real-time data warehouse, EAI, BAM performance management
  • Persistence, need and physical nature of data staging
  • Physical instantiation of operational data stores - single-source, multi-source
  • Program development team engineering technology selection process - framework, best-of-breed
  • Source work effort distribution - source team, data warehouse team, shared
  • Use of operational data stores for source systems - selective, complete

Especially for "large" (or potentially large) data warehouses, I favor EDW architecture and a quasi-bottoms-up methodology. It's like how I conclude the presentation: "The best approach seems to be a hybrid quasi enterprise architecture and hybrid, but rapid, development methodology with some up-front work and use of standards with broadly defined enterprises and federation techniques used to unite large enterprises."


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