Battle Of The Giants Comparing Kimbal
DW MODELS
Data Warehousing
Battle of the Giants:
Comparing the Basics of the
Kimball and Inmon Models
Mary Breslin
Many organizations today need to create data warehouses— massive data stores of time-series data used for decision support.
These organizations face a range of choices, both in terms software tools and development approaches. Making good choices requires an understanding of the two main data warehousing models—
Inmon’s and Kimball’s.
Bill Inmon advocates a top-down development approach that adapts
Mary Breslin has worked in both user and IT roles and she is currently exploring Capella University’s data warehouse from the user side. marybreslin@earthlink.net
traditional relational database tools to the development needs of an enterprisewide data warehouse. From this enterprisewide data store, individual departmental databases are developed to serve most decision support needs.
Ralph Kimball, on the other hand, suggests a bottom-up approach that uses dimensional modeling, a data modeling approach unique to data warehousing. Rather than building a single enterprisewide database,
Kimball suggests creating one database (or data mart) per major business process. Enterprisewide cohesion is accomplished by using another Kimball innovation, a data bus standard.
Understanding how these two models are similar and how they differ gives the reader a foundational knowledge of the most basic data warehouse concepts. We will also explore which organizational characteristics are best suited to each approach.
6
BUSINESS INTELLIGENCE JOURNAL • WINTER 2004
DW MODELS
Introduction and Context
We begin our discussion by defining the data warehouse.
We will introduce the Inmon-Kimball debate, and provide a brief history of the evolution of the two models.
We also provide a brief explanation of the nature of the data warehouse, and conclude with a discussion of the scope of the article.
Context of the Inmon-Kimball Debate
A data warehouse contains massive amounts of
Data Warehousing
Battle of the Giants:
Comparing the Basics of the
Kimball and Inmon Models
Mary Breslin
Many organizations today need to create data warehouses— massive data stores of time-series data used for decision support.
These organizations face a range of choices, both in terms software tools and development approaches. Making good choices requires an understanding of the two main data warehousing models—
Inmon’s and Kimball’s.
Bill Inmon advocates a top-down development approach that adapts
Mary Breslin has worked in both user and IT roles and she is currently exploring Capella University’s data warehouse from the user side. marybreslin@earthlink.net
traditional relational database tools to the development needs of an enterprisewide data warehouse. From this enterprisewide data store, individual departmental databases are developed to serve most decision support needs.
Ralph Kimball, on the other hand, suggests a bottom-up approach that uses dimensional modeling, a data modeling approach unique to data warehousing. Rather than building a single enterprisewide database,
Kimball suggests creating one database (or data mart) per major business process. Enterprisewide cohesion is accomplished by using another Kimball innovation, a data bus standard.
Understanding how these two models are similar and how they differ gives the reader a foundational knowledge of the most basic data warehouse concepts. We will also explore which organizational characteristics are best suited to each approach.
6
BUSINESS INTELLIGENCE JOURNAL • WINTER 2004
DW MODELS
Introduction and Context
We begin our discussion by defining the data warehouse.
We will introduce the Inmon-Kimball debate, and provide a brief history of the evolution of the two models.
We also provide a brief explanation of the nature of the data warehouse, and conclude with a discussion of the scope of the article.
Context of the Inmon-Kimball Debate
A data warehouse contains massive amounts of
References: Albert, G. The Importance of Data Warehousing (May 03, 2000), BusinessLine, Internet edition, division of The Hindu Business Line. Retrieved August 12, 2003, from http://www.blonnet.com/businessline/2000/05/03/stories/ (April 2003), TDWI Web site. Retrieved August 11, 2003, from http://www.dw-institute.com/research/. Inmon, W.H. Building the Data Warehouse (Third Edition), New York: John Wiley & Sons, (2002). Inmon, W.H. and C. Imhoff. Corporate Information Factory Components, (2002), Inmon Associates Inc site. Retrieved September 9, 2003 from http://www.billinmon.com/library/articles/artcifco.asp. Inmon, W.H. Accelerating the Development of the Enterprise Data Warehouse, (2000), Inmon Associates Inc. Web site. Retrieved September 10, 2003 from http://www.billinmon.com/library/presents/present.asp. Wells, D., Choosing the Right Data Warehousing Approach, (January 3, 2003),TDWI FlashPoint, TDWI Web site. Retrieved July 24, 2003, from http://www.dwinstitute.com/research/. Wells, D. Making Sense of the Methodology Debate, (August 27, 2003a), TDWI FlashPoint, TDWI Web site. Retrieved September 7, 2003, from http://www.dw-institute.com/research/. Whiting, R. Startup Netezza Pushes Discount Data Warehouse Products, (September 23, 2002), CommWeb,