Chapter 9: Bells and WhistlesSpecial Features of OLAP Cubes

I do not like poems that resemble hay compressed into a geometrically perfect cube.
—Ycvgeny Ycvtushcnko

Overview

Thus far, we have created two data marts for our sample company, Maximum hus far, we have created two data marts for our sample company, Maximum Miniatures, Incorporated. Both data marts are full of data (assuming you used the SalesDMDimLoad projects and SalesDMFactLoad solutions to populate the Sales data mart). We also have an OLAP cube built on top of each of these data marts. You may think our work is almost done. On the contrary, it is just beginning!

We have data sitting in data marts in relational databases. We have cube definitions that exist as XML definition documents in our development environments. What we do not have is multidimensional cubes filled with preprocessed aggregates waiting to help us create business intelligence. (In case you forgot after reading the first few chapters, that is why we are here.)

We begin by taking care of this very issue. We can deploy our cubes to SQL Server Analysis Services databases. We can then begin querying aggregates from the cubes and begin to see benefit from our labors.

Once the cubes are deployed, we examine many of the special features available in these OLAP cubes. We move between the Business Intelligence Development Studio and the SQL Server Management Studio to view the data and try out the development tools at our disposal.