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As you learned previously in this chapter, the DSV is a very flexible structure that can be modified as you add dimensions and cubes to your project You can add tables to the DSV at any time In addition, you can add a Named Query to limit the columns available from a single table or to construct a logical view by combining columns from multiple tables As another alternative, you can add a derived column, known as a Named Calculation, to a table Using a Named Query or a Named Calculation gives you the ability to manipulate the data structures for use by Analysis Services even if you don t have permissions to make similar changes at the database level.

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Once you have determined when processing should occur, you need to think about what data will be processed. The easiest approach is to do a full process each time, which rebuilds the physical structures and reloads all data from the partition s source table or source query. If any problems occur during processing, the database is rolled back to its original state. Performing a full process is a perfectly reasonable approach if you have small data volumes or if you need to process relatively infrequently. Another approach particularly well-suited for partitioned cubes is incremental processing. As with full processing, you can use an Integration Services task or scripting to manage incremental processing. If your partitions are filtered by time slices, such as by year or by month, you need only to load data into the most current partition and process that partition independently. Although you can do a full process of the single partition, you can optimize processing performance by configuring an incremental process to use a query that selects records for loading into the partition. Typically, this query returns a relatively small number of records that can be easily appended to existing records in the partition. Aggregations are also updated during processing. The most important thing to know about incremental processing is that you must be extremely careful not to duplicate data already in the partition. More Info There are several more processing options available. A good reference describing these options and the impact of processing on dependent objects is available at http://msdn.microsoft.com/library/default.asp url=/library/enus/dnsql90/html/sql2k5_asprocarch.asp.

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when you designed the database If you change the schema for example, if you add a measure or a dimension to a cube you must process at least the affected portions of the database You also need to process at least a portion of the database if the information in the data warehouse changes, as it inevitably will The information in a data warehouse is almost always time-dependent That means that, at the very least, you ll continually add new time periods to your data warehouse In time, you might also add additional products or additional sales regions When the data warehouse changes, you need to process the database to resynchronize the OLAP database with the relational data warehouse The FactResellerSales table in the SSAS Step by Step DW database contains data for six countries through June 2004.

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If you use Analysis Services to generate a Server Time dimension for you based on a range of dates, you need to use a Date/Time column in the fact table to use this dimension (Creating a Server Time dimension is discussed later in this chapter) However, it s generally recommended that you use a separate dimension table for dates rather than use a Date/Time column in the fact table As with other dimension tables, you would then use an integer key to join the date dimension table with the fact table This difference in data types in the fact table date/time which requires 8 bytes versus integer which requires 4 bytes can have a big impact on the amount of space required to store the fact table in your data warehouse when the fact table contains millions of records.

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In the SSAS Step by Step database, the DimSalesTerritory dimension table includes only the six countries that appear in the fact table The DimTime dimension table, however, includes months through August 2004 It is not uncommon in a warehouse to include months in the time dimension through the end of the current quarter or year, but to add members to other dimensions only as they are needed Included in the as2005sbs\chap12 folder is a SQL script named Update Warehousesql This script adds one new entry to the DimSalesTerritory table (Mexico) and additional rows in the fact table for sales in Mexico and in Australia for June 2004 Inserting these records into the source database for the cubes simulates, on a very small scale, the load operations that occur regularly in a production data warehouse.

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