Database Modeling & Design Fourth Edition- P34: Database technology has evolved rapidly in the three decades since the rise and eventual dominance of relational database systems. While many specialized database systems (object-oriented, spatial, multimedia, etc.) have found substantial user communities in the science and engineering fields, relational systems remain the dominant database technology for business enterprises. | 152 CHAPTER 8 Business Intelligence 1998 2002 have a series of excellent books covering the details of data warehousing activities. Figure outlines the activities of the data warehouse life cycle based heavily on Kimball and Ross s Figure 2002 . The life cycle begins with a dialog to determine the project plan and the business requirements. When the plan and the requirements are aligned design and implementation can proceed. The process forks into three threads that follow independent timelines meeting up before deployment see Figure . Platform issues are covered in one thread including technical architectural design followed by product selection and installation. Data issues are covered in a second thread including dimensional modeling and then physical design followed by data staging design and development. The special analytical needs of the users are pursued in the third thread including analytic application specification followed by analytic application development. These three threads join before deployment. Deployment is followed by maintenance and growth and changes in the requirements must be detected. If adjustments are needed the cycle repeats. If the system becomes defunct then the life cycle terminates. The remainder of our data warehouse section focuses on the dimensional modeling activity. More comprehensive material can be found in Kimball and Ross 1998 2002 and Kimball and Caserta 2004 . Logical Design We discuss the logical design of data warehouses in this section the physical design issues are covered in volume two. The logical design of data warehouses is defined by the dimensional data modeling approach. We cover the schema types typically encountered in dimensional modeling including the star schema and the snowflake schema. We outline the dimensional design process adhering to the methodology described by Kimball and Ross 2002 . Then we walk through an example covering some of the crucial concepts of dimensional data .
