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10. In Visual Studio, on the File menu, click Close Project, and then, on the File menu, click New, and then Project. 11. In the New Project dialog box, click Import Analysis Services 9.0 Database, type SSAS in the Name box, type C:\Documents and Settings\<username>\My Documents \Microsoft Press\as2005sbs\Workspace in the Location box (replacing <username> with your login name), and then click OK. 12. Click Next on the Welcome page of the Import Analysis Services 9.0 Wizard, select SSAS Step by Step in the Database drop-down list, click Next, and then click Finish when the import is complete. 13. Open the SSAS cube, and then click the Partitions tab. Your screen looks like this:
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You can see that there are only two partitions Reseller Sales through 2003 and Reseller Sales 2004 defined for the Reseller Sales measure group.
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Analysis Services always stores information about database objects cubes, partitions, dimensions, for example in data files on the server. You can think of this layer of information as a map. For each partition in a cube, though, you decide where to store the detail and aggregated values. You can choose from three physical storage options: ROLAP, HOLAP, and MOLAP.
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ROLAP, for relational OLAP, leaves detail values in the relational fact table and stores aggregated values in the relational database as well.
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12:
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HOLAP, for hybrid OLAP, leaves the detail values in the relational fact table but stores the aggregated values in the cube. MOLAP, for multidimensional OLAP, stores both detail and aggregated values within the cube.
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All three storage modes include the cube map within Analysis Services. It s the cube map that makes the data appear as a cube to a person running a query. That means the storage mode is invisible to client applications that is, applications that query the cube. The client application always sees the cube. The storage option you choose affects only performance. Because a client application can t tell which storage mode you have chosen, you can change the storage mode without affecting any client applications. Once you specify storage and start using the cube, you can change your mind later and switch to a different storage type. Because a cube appears to the client application as a single, logical entity, you can use different storage modes for different portions of a cube. In order to do that, you must use multiple partitions. Note Regardless of which storage option you choose, Analysis Services will never allocate storage for missing values. For example, if you have a database that shows you didn t start selling products in Australia until 2003, Analysis Services will use no storage space for detail or aggregated values for Australia in 2003.
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Understanding Analysis Services Storage Modes
Choosing a storage mode is not as difficult as it might seem. In the first place, using Analysis Services to store aggregations in a relational database never makes any sense, so you should never choose the ROLAP with aggregations option. Aggregations in a relational database are both bulky and slow. The purpose of creating aggregations is to improve performance, and relational aggregations defeat the purpose. The only reason you might choose the ROLAP option is if you re learning about aggregations and want to physically look at aggregations or if your analytical solution requires near real-time data. ROLAP aggregations are completely unusable by any application other than Analysis Services. Aggregations in both MOLAP and HOLAP are identical, so the only difference is where the detail-level values are stored. If you count the space required by the original warehouse as well as the space needed for the OLAP cubes, MOLAP does consume more storage space than HOLAP because the MOLAP storage option does duplicate the values from the fact table. Analysis Services, however, is efficient in how it stores data. For example, the SSAS Step by Step DW database requires approximately 37 MB of storage space. A cube containing the same level of detail (with no aggregations) takes only 14 MB of storage space! With a very large warehouse database, you could process the data into a MOLAP cube and then archive and remove the original warehouse. By using the MOLAP storage option, you could actually end up using a fraction of the original storage space.
Part IV:
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