An OLAP Requirements Example: CompSales International (part 9) - Browsing Data in the Cube

Browsing Data in the Cube

You’re ready to browse some cube data now. There are several ways to view data in a multidimensional cube. OLE DB for OLAP and ADO MD expose interfaces to do this kind of data browsing, and many leading vendors have used these interfaces to build front-end analysis tools and ActiveX controls. These tools should prove useful for developers of user interfaces in data warehousing and data mart projects. You can also easily browse a cube’s data from either Visual Studio or SSMS or via any tool or facility that uses the multidimensional extensions of SQL (that is, SQL with DMX and MDX extensions).

To browse your newly created cube from SSMS, you fire up SSMS and connect to the SSAS server (Analysis Services server type) on which you deployed your cube. You should not connect to the SQL Server Database Engine. These are two completely different servers. When you are connected, you expand the Databases tree on the left until you can see the cube you created (Comp Sales, in this example).

In SSMS, you just right-click the Comp Sales cube entry and choose the Browse option. As you can see in Figure 41, a multipaned, drag-and-drop interface is your view into the data in your cube.

Figure 41. Browsing data in your cube in the SMSS data browser.

The middle pane lists all cube objects that you can drag into the data browsing pane (on the right). The data browser uses the Pivot Table Service to access and display your cube’s data. You can expand any of the cube hierarchy objects and see the actual member entries that are in your cube for each level. This capability is helpful when you want to further filter data in the browser (for example, focus on a particular SKU value or a particular geography, such as United States or France).

The data browsing pane is easy to use. For example, say that you simply want to see all product sales and product returns for SKUs across all geographies, for each year in the cube. To do this, you expand the measures object until you see all the measures in the Comp Sales cube. Then you drag Sales Units to the center of the lower portion of the data browsing pane (into the Drop Totals or Detail Fields Here section in the lower right). You do the same for the Sales Returns measure. Data values (totals) for these measures are already displayed immediately. These are the total (aggregated) values for sales returns and sales units across all products, all geographies, and all times. To see the product breakdown of these data measures, you drag the SKU object within the product dimension object to the Drop Column Fields Here section (just above where the data measures were dropped). You immediately see the data measure values being broken out by each product SKU value. Now, you drag the Year Time object within the time dimension to the Drop Row Fields Here section (just to the left of where the data measures were dropped). You now see the data broken out by the years along the left side (rows) in the cube that contains sales and return data for products, as shown in Figure 42.

Figure 42. Sales units and sales returns for all SKUs by years in the SMSS data browser.

If you want much more drill-up and drill-down visibility into your data, you could build up a much more complicated representation in the data browser. Say that you want to see sales units and sales returns but across the full product dimension breakouts and full time dimension breakouts for the United States geographic region only. You also want to see all dimension levels, totals by levels, and grand totals by dimension. You start the same way as you did earlier and expand out the measures object until you see all the detail measures in the Comp Sales cube. If you still have the previous example in your data browser, you can simply locate the Clear Results icon in the data browser tab and clear the data browser pane. Then you drag Sales Units to the center of the lower portion of the data browsing pane (into the Drop Totals or Detail Fields Here section in the lower right). You do the same for the Sales Returns measure. Then you drag the geography dimension to the upper section called Select Dimensions or just highlight Select Dimensions and choose the geography dimension. This is the dimension-level filtering capability within the data browser. You now just select (via the drop-downs of each section within a filter specification) the level and type of filtering you want to do for the dimension you are working with. You can specify any number of filters within any number of dimensions. To just filter on countries within the geography dimension, you select Countries within the hierarchies list of the geography dimension, and the operator you want is Equal, and the filter expression is the data value that you want to filter on (the United States country value, in this case). These are all drop-down lists that you can easily select by either clicking the entry or indicating which ones to use via a check box entry. Figure 43 shows the fully specified Geo Dimension filter specified.

Figure 43. Complex data browsing with full dimensions and filtering in the SMSS data browser.

The data values you now see are only those of the United States. You now drag the product dimension object to the Drop Column Fields Here section (just above where the data measures were dropped). You immediately see the data measure values being broken out by the entire product dimension (you expand the plus sign of the product hierarchy all the way out to the SKU level). Then you drag the time dimension object to the Drop Row Fields Here section (just to the left of where the data measures were dropped). You can choose to view the data at any level within either the time or product hierarchies, and you can filter on any other dimension values. You can also just add a dimension or dimension level to the filter portion within the data browser or just drag off dimensions, measures, or filters from the data browser if you don’t want to use them anymore. This is very easy indeed. The cube browser shows you what your cube has in it and also illustrates the utility of a dimensional database. Users can easily analyze data in meaningful ways.

SSAS allows you to browse individual dimension member data. You just right-click any dimension in the left pane of SSMS (for example, the time dimension) and choose Browse. As you can see in Figure 44, the dimension browser opens with All as the top node in the dimension. You simply expand the levels to see the actual member values within this cube dimension. Expanding each level gets you to more detailed information as you move down the dimension hierarchy.

Figure 44. Browsing the Time dimension using SSMS.