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DATA WAREHOUSING FUNDAMENTALS: A Comprehensive Guide for IT Professionals by Paulraj Ponniah

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11.1. CHAPTER OBJECTIVES

  • Discuss and get a good grasp of slowly changing dimensions

  • Understand large dimensions and how to deal with them

  • Examine the snowflake schema in detail

  • Learn about aggregate tables and determine when to use them

  • Completely survey families of STARS and their applications

From the previous chapter, you have learned the basics of dimensional modeling. You know that the STAR schema is composed of the fact table in the middle surrounded by the dimension tables. Although this is a good visual representation, it is still a relational model in which each dimension table is in a parent–child relationship with the fact table. The primary key of each dimension table, therefore, is a foreign key in the fact table.

You have also grasped the nature of the attributes within the fact table and the dimension tables. You have understood the advantages of the STAR schema in decision support systems. The STAR schema is easy for the users to understand; it optimizes navigation through the data warehouse content and is most suitable for query-centric environments.

Our study of dimensional modeling will not be complete until we consider some more topics. In the STAR schema, the dimension tables enable the analysis in many different ways. We need to explore the dimension tables in further detail. How about summarizing the metrics and storing aggregate numbers in additional fact tables? How much precalculated aggregation is necessary? The STAR schema is a denormalized design. Does ...

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