An unparalleled collection of recommended guidelines for data warehousing and business intelligence pioneered by Ralph Kimball and his team of colleagues from the Kimball Group.

Recognized and respected throughout the world as the most influential leaders in the data warehousing industry, Ralph Kimball and the Kimball Group have written articles covering more than 250 topics that define the field of data warehousing. For the first time, the Kimball Group's incomparable advice, design tips, and best practices have been gathered in this remarkable collection of articles, which spans a decade of data warehousing innovation.

Each group of articles is introduced with original commentaries that explain their role in the overall life-cycle methodology developed by the Kimball Group. These practical, hands-on articles are fully updated to reflect current practices and terminology and cover the complete life-cycleΓÇöincluding project planning, requirements gathering, dimensional modeling, ETL, and business intelligence and analytics.

This easily referenced collection is nothing less than vital if you are involved with data warehousing or business intelligence in any capacity.



The Kimball Group Reader covers:

    * Dos and Don'ts for gathering requirements
    * The Kimball enterprise data warehouse architecture and matrix
    * Dimensional modeling tasks and responsibilities
    * Fact table and dimension table core concepts
    * Data stewardship for conforming dimensions
    * Granularity and three fundamental types of fact tables
    * Snowflakes and outriggers
    * Bridges for handling multi-valued dimension attributes and ragged variable hierarchies
    * Nuances encountered in customer dimensions and internationalization issues
    * Slowly changing dimensions and hybrid variations
    * Subsystems to extract, transform, and load (ETL) data
    * Pros and cons of using a commercial ETL tool
    * Server-oriented architecture, master data management, and packaged analytics
    * Front-room BI applications and data mining
    * Techniques for assessing a mature DW/BW environment and getting it back on track