Chapter 12 - Chapter 12. Warehouse Schema Design...

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Chapter 12. Warehouse Schema Design Dimensional modeling is a term used to refer to a set of data modeling techniques that have gained popularity and acceptance for data warehouse implementations. The acknowledged guru of dimensional modeling is Ralph Kimball, and the most thorough literature currently available on dimensional modeling is his book entitled The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses, This chapter introduces dimensional modeling as one of the key techniques in data warehousing and is not intended as a replacement for Ralph Kimball's book. OLTP Systems Use Normalized Data Structures Most IT professionals are quite familiar with normalized database structures, since normalization is the standard database design technique for the relational databases of Online Transactional Processing (OLTP) systems. Normalized database structures make it possible for operational systems to consistently record hundreds of thousands of discrete, individual transactions, with minimal risk of data loss or data error. Although normalized databases are appropriate for OLTP systems, they quickly create problems when used with decisional systems. Users Find Normalized Data Structures Difficult to Understand Any IT professional who has asked a business user to review a fully normalized entity relationship diagram has first-hand experience of this problem. Normalized data structures simply do not map to the natural thinking processes of business users. It is unrealistic to expect business users to navigate through such data structures. If business users are expected to perform queries against the warehouse database on an ad hoc basis and if IT professionals want to remove themselves from the report-creation loop, then users must be provided with data structures that are simple and easy to understand. Normalized data structures do not provide the required level of simplicity and friendliness. Normalized Data Structures Require Knowledge of SQL To create even the most basic of queries and reports against a normalized data structure requires knowledge of SQL (Structured Query Language)—something that should not be expected of business users, especially decision-makers. Senior executives should not have to learn how to write programming code, and even if they knew how, their time is better spent on nonprogramming activities. Unsurprisingly, the use of normalized data structures results in many hours of IT resources devoted to writing reports for operational and decisional managers. Normalized Data Structures Are Not Optimized to Support Decisional Queries By their very nature, decisional queries require the summation of hundreds to tens of thousands of figures stored in perhaps as many rows in the database. Such processing on a fully normalized data structure is slow and cumbersome.
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Consider the sample data structure in Figure 12-1 . Figure 12-1 Example of a Normalized Data Structure
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Chapter 12 - Chapter 12. Warehouse Schema Design...

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