By Guy Fouche and Lynn Langit
Foundations of SQL Server 2008 R2 enterprise Intelligence introduces the interesting gamut of industrial intelligence instruments incorporated with SQL Server 2008. Microsoft has designed SQL Server 2008 to be greater than only a database. It’s a whole enterprise intelligence (BI) platform. The database is on the middle, and surrounding the database are instruments for info mining, modeling, reporting, interpreting, charting, and integration with different, enterprise-level software program packages.SQL Server 2008 places an important quantity of BI performance at your disposal. yet how do you are taking benefit of it? That’s what this publication is all approximately. Authors man Fouch# and Lynn Langit express the right way to enforce end-to-end BI strategies utilizing SQL Server research providers (SSAS), SQL Server Integration companies (SSIS), SQL Server Reporting providers (SSRS), and different instruments within the Microsoft BI toolkit. You’ll find out about all-new positive factors similar to PowerPivot and file Builder 3.0. additionally supplied are transparent examples of predictive research made attainable via robust info mining gains in SQL Server. [
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Extra resources for Foundations of SQL Server 2008 R2 Business Intelligence
Use this dialog of the Data Source Wizard to confirm your connection settings. Creating a DSV is similar to creating a data source. To create a DSV, right-click the Data Source Views folder in Solution Explorer, and select New Data Source View. Click Next in the Welcome dialog. The Select a Data Source dialog will contain the data source you just created. Select this data source, and click Next. In the Select Tables and Views dialog, select the following tables from the “Available objects” 56 CHAPTER 3 ■ INTRODUCING OLAP MODELING WITH SSAS area, and place them into the Included objects: area, using the right-facing arow button or by drag-anddrop: • DimCustomer • DimDate • DimEmployee • DimGeography • DimProduct • DimProductCategory • DimProductSubcategory • FactInternetSales • FactResellerSales Figure 3–4 displays the nine tables that you will be using to create your first cube.
This is modeled in the preferred method discussed earlier in this chapter. Figure 2–7 shows the fact tables for the same project. You can see that there are nearly as many fact tables as dimension tables in this particular model example. This isn’t necessarily common in OLAP model design; more commonly, you’ll use 1 to 5 fact tables with 5 to 15 dimension tables, or more of both types. This model is used to illustrate reasons for using multiple fact tables; for example, some Session types have facts by day, whereas other Session types have facts by hour.
The Dimension Usage section of SSAS usually reflects the snowflake relationship, which you have modeled when you initially create the cube using the New Cube wizard (as long as the key columns have the same names across all related tables). If necessary, you can manually adjust any relationships after the cube has been created using tools provided in BIDS. As shown previously in this chapter, Figure 2–9 again shows the Dimension Usage grid. This time, we are going to drill down a bit deeper into using it.