Dimensional modeling : in a business intelligence by Chuck Ballard; International Business Machines Corporation.

By Chuck Ballard; International Business Machines Corporation. International Technical Support Organization.; et al

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Introduction 17 Chapter 9, “Managing the meta data”, includes: – Information on meta data. Having emphasized the importance of the dimensional model, now take a step back. Meta data is the base building block for all data. It defines and describes the data, and gives it structure and content. – Definitions and descriptions of meta data types and formats. It also provides a discussion about meta data strategy, standards, and design. – An overview of tools used to work with meta data. Chapter 10, “SQL query optimizer: A primer”, includes: – A review of relational database server disk, memory, and process architectures, which includes the relational database server multistage back-end.

However, there are also many other data structures that can be part of the data warehousing environment and used for data analysis, and they use differing implementation techniques. These fall in a category we are simply calling analytic structures. However, based on their purpose, they could be thought of as data marts. They include structures and techniques, such as: Materialized query tables (MQT) Multidimensional clustering (MDC) Summary tables Spreadsheets OLAP (Online Analytical Processing) databases Operational data stores Federated databases Although data marts can be of great value, there are also issues of currency and consistency.

We also introduced the two primary techniques used in data modeling (E/R and Dimensional), positioned them, and then stated that in this redbook we have a specific focus on dimensional modeling. However, it is good to keep in mind the bigger picture and understand why we are so interested in the topic of dimensional modeling. Simply put, it is because the data model is the base building block for any data structure. In our case, the data structure is a data warehouse. And, the data warehouse is the base building block that supports business intelligence solutions - which is really the goal we are trying to achieve.

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