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Chapter 1: Introduction to OLAP and Its Role in Business Intelligence


By Irina Gorbach, Alexander Berger, Edward Melomed, Network World
February 02, 2009 09:44 PM ET

In This Chapter

  • The Multidimensional Data Model

In the past decade, Microsoft SQL Server Analysis Services established itself as one of the leaders in the Business Intelligences systems market. Analysis Services helps managers, employees, customers, and partners to make more informed business decisions by enabling them to analyze information accumulated during a company’s day-to-day operations.

Success of Analysis Services and the entire Business Intelligence market was predefined by incredible growth of amounts of data accumulated as a result of everyday functioning of a large number of companies. Today it’s hard to imagine a business or an organization that doesn’t use an online transaction processing (OLTP) system. OLTP systems provide means to highly efficient execution of a large number of small transactions and reliable access to data stored in the result of the transactions.

The volume of the data stored and processed for one day by an OLTP system could be several gigabytes per day; after a period of time, the total volume of data can reach to the tens and even hundreds of terabytes. Such a large volume of data can be hard to store, but it is a valuable source of information for understanding the way the enterprise functions. This data can prove very helpful for making projections that lead to successful strategic decisions, and for improving everyday decision making.

It’s easy to see why analysis of data has become so important to the management of modern enterprises. However, OLTP systems are not well suited to analyzing data. In the past decades, an entire new market has emerged for systems that can provide reliable and fast access for analyzing very large amounts of data: online analytical processing (OLAP). OLAP enables managers, executives, and analysts to gain insight into data using fast, interactive, and consistent interfaces to a wide variety of possible views of information. For example, with OLAP solution, you can request information about company sales in Europe over the year, then drill down to the sales of computers in September, calculate year-to-date sales or compare revenue figures with those for the same products sold in January, and then see a comparison of TV sets sales in Europe in the same time period.

Because OLAP systems are designed specifically for analysis, they typically don’t need to both read and write data. All that is necessary for analysis is reading data. With this emphasis on reading only, OLAP systems enjoy a speed advantage over their OLTP cousins. However, a read-only approach to the database architecture is not the only distinction of the OLAP solution. The following rules distinguish OLAP systems from relational databases:

  • Multidimensional data structures
  • OLAP solutions typically use multidimensional data structures that allow analysts and managers to analyze numeric values from different perspectives, such as time, customers, products, and others.
  • Consistently fast data access
  • Architecture of the system allows constantly fast access to the data. To ensure fast, predictable query times, OLAP solutions typically pre-aggregate data.
  • Intuitive interface
  • Skilled analysts and nontechnical users alike can manipulate and analyze data; they can generate reports without involving their organization’s IT department.
  • Complex calculations
  • With multiple dimensions come more complex, cross-dimensional calculations. You might need to calculate the subtotal of sales for the state Washington, for example, to be expressed as a percentage of the whole U.S. sales. Further, this result may be presented as part of a time-series analysis (for instance, current month versus last month, versus a year ago).

The Multidimensional Data Model

The design and development of the multidimensional database—especially Microsoft SQL Server Analysis Services, the system designed and developed by the authors of this book—was inspired by the success of relational databases. If you’re already familiar with relational databases, you’ll recognize some of the terminology and architecture. But, to understand Analysis Services, you must first understand multidimensional data models, how this model defines the data and processes it, and how the system interacts with other data storing systems, primarily with the relational data model.

The multidimensional data model for Analysis Services consists of three more specific models:

  • The conceptual data model

  • The application data model

  • The physical data model

The Conceptual Data Model

The conceptual data model contains information about how the data is represented and the methods for defining that data. It defines data in terms of the tasks that the business wants to accomplish using the multidimensional database. To define conceptual data model, you use the user specifications for the structure and organization of the data, rules about accessing the data (that is, security rules), and calculation and transformation methods.

In a sense, the conceptual data model serves as a bridge between a business model and the multidimensional data model. The solutions architect is the primary user for the conceptual data model. We use Data Definition Language (DDL) and MDX (Multidimensional Extensions) script for the creation of the conceptual model. You can also use Business Intelligence Development Studio to develop the conceptual data model.

The Application Data Model

The application model defines the data in a format that can be used by the analytical applications that will present data to a user in a way that he can understand and use. The primary user for the application data model is the client application, which exposes the model to the user. The application model is built with the MDX language and XML for Analysis protocol. The chapters of Part 3, “Using MDX to Analyze Data,” contain detailed information about MDX and a few of most commonly used client applications. The chapters of Part 7, “Accessing Data in Analysis Services,” contain information about protocol used by Analysis Services to communicate with client applications.

The Physical Data Model

As in the arena of relational databases, the physical model defines how the data is stored in physical media:

  • Where it is stored—What drive (or maybe on the network), what types of files the data is stored in, and so on

  • How it is stored—Compressed or not, how it’s indexed, and so on

  • How the data can be accessed—Whether it can be cached, where it can be cached, how it is moved into memory, and so on

The database administrator is the primary user for the physical data model. We use XML-based commands for manipulation of data on the physical layer.

Figure 1.1 shows relationships between three parts of multidimensional model.

Figure 1.1
Submodels of the multidimensional model.

You use SQL Server Business Intelligence Development Studio or SQL Server Management Studio to define a conceptual data model, also known as a Unified Dimensional Model (UDM) or cube. After the conceptual model is defined, you populate it with data by loading/processing the data from the relational database. At this time, you define the physical data model—partitioning scheme of the data, indexing scheme, and so on. The application model of Analysis Services consists of standard data access interfaces. Client applications use those interfaces: XML for Analysis and MDX to communicate with Analysis Services. More than hundred applications available today support the application model of Analysis Services and can work with any Analysis Services cubes.

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