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This chapter is from the book

This chapter is from the book

OLAP Concepts and OLAP Reporting

OLAP is an analysis-oriented technology that enables rapid analysis of large sets of aggregated data. Instead of representing information in the common two-dimensional row and column format of traditional relational databases, OLAP databases store their aggregated data in logical structures called cubes. Designers create OLAP cubes around specific business areas or problems. Cubes contain an appropriate number of dimensions to satisfy analysis in that particular area of interest or for a specific business issue. OLAP is a technology that facilitates data viewing, analysis, and navigation. More than a particular storage technology, OLAP is a conceptual model for viewing and analyzing data. Table 16.1 highlights some common business areas and typical sets of related dimensions.

Table 16.1. Business Areas and Commonly Associated OLAP Dimensions

Business Area

Associated Business and Common OLAP Dimensions

Sales

Sales Employees, Products, Regions, Sales Channels, Time, Customers, Measures

Finance

Company Divisions, Regions, Products, Time, Measures

Manufacturing

Suppliers, Product Parts, Plants, Products, Time, Measures

OLAP cubes pre-aggregate data at the intersection points of their associated dimension's members. A member is a valid field value for a dimension. For example, members of a time dimension could be 2006, 2007, Q1, or Q2; members of a product dimension could be Gadget1, Gizmo2, DooDah1, and so on. This pre-aggregation facilitates the speed-of-thought analysis associated with OLAP.

Precalculating the numbers at the intersection points of an OLAP cube's associated dimension members enables rapid high-level analysis of large volumes of underlying data that would not be practical with traditional relational databases. Consider the example of analysis on several years of sales data by year, quarter, and month and by region, sales manager, and product. The pre-aggregated nature of OLAP facilitates speed-of-thought analysis that otherwise would not be practical when working with the phenomenal amount of data and involved calculations required to provide answers on a traditional relational (OLTP) database system—it would simply take too long.

When a Crystal Report uses an OLAP cube as a data source, it presents the multidimensional data in a two-dimensional OLAP grid that resembles a spreadsheet or cross-tab. The focus of Crystal Reports when reporting against OLAP cubes is to present professionally formatted two-dimensional (or flat) views of the multidimensional data of particular business use for report-consuming end users and not necessarily analysts requiring interactivity—the more traditional OLAP end users.

The concepts of OLAP usually become more understandable after you explore them. To that end, later sections in this chapter step you through a Crystal Reports report creation example against an OLAP cube.

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