Читать книгу Applied Microsoft Business Intelligence - Sarka Dejan - Страница 4

Now that a database engine is selected to host the data warehouse, the decision to build an analytical model or, in the case of a Microsoft Solution, semantic model must be made. With the latest release of SQL Server, semantic models have three choices from which you can select:

● Power Pivot

● Tabular

● Multidimensional

So not only must you decide how to build a semantic model, but also which model to use.

If the business intelligence solution requires very fast response times, ad-hoc capabilities, or predictive analytics, leveraging SQL Server Analysis Services (SSAS) is a great option. Whereas the aforementioned list is not inclusive of all factors that may drive the need for a semantic model, they definitely make a strong case in favor of it. SSAS offers a wide range of capabilities that assist in streamlining and reducing report requests, centralizing analytical formulas and key performance indicators, and – probably one of the more important robust capabilities – intuitively handling security at different levels. Figure 1.3 illustrates a business intelligence solution that includes a semantic model. Notice how the reporting tools are expanded when you compare them with Figure 1.2.

Figure 1.3 Business intelligence solution that includes SSAS semantic model

Although it is possible to report directly against a data warehouse using Excel and Performance Point (discussed later in the chapter), SSAS provides a more innate design experience with these tools. In addition, using SSAS provides end users with a larger surface of self-service capabilities that are unavailable when only a data warehouse is available. Therefore, they are excluded from Figure 1.2, but included in Figure 1.3.

For example, if you are the CEO of a company, you may require access to every aspect of data in the model. However, if you are a regional or departmental manager, you may only require access to data that is pertinent to your region or department. SSAS includes built-in capabilities that let you control access to data at the row level. In many cases, this is one of the most important and often overlooked requirements of a business intelligence solution. During most projects, you don't realize this until very late in the development process. However, when using SSAS, the implementation process is neither very difficult nor disruptive.