McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Data Warehouse: additional slides Source: Michael V. Mannino, Database: Design, Application Development & Administration, Third Edition, McGraw Hill, 2007

16-2 Data Comparison

16-3 Applications

16-4 Example: Star Schema

16-5 Example: Input table records Sales Table SalesNoSalesDollorTimeNoStoreId TimeDim Table TimeNoTimeMonthTimeYear Store Table StoreIdStoreStateStoreNationStoreZip 1001MNUSA MNUSA MNUSA OHUSA OHUSA OHUSA80112

16-6 CUBE Operator Example SELECT StoreZip, TimeMonth, SUM(SalesDollar) AS SumSales FROM Sales, Store, TimeDim WHERE Sales.StoreId = Store.StoreId AND Sales.TimeNo = TimeDim.TimeNo AND (StoreNation = 'USA' OR StoreNation = 'Canada') AND TimeYear = 2005 GROUP BY CUBE (StoreZip, TimeMonth)

16-7 CUBE Operator Example Output of query with CUBE operator StoreZipTimeMonthSumSales

16-8 ROLLUP Operator Example SELECT StoreZip, TimeMonth, SUM(SalesDollar) AS SumSales FROM Sales, Store, TimeDim WHERE Sales.StoreId = Store.StoreId AND Sales.TimeNo = TimeDim.TimeNo AND (StoreNation = 'USA' OR StoreNation = 'Canada') AND TimeYear = 2005 GROUP BY ROLLUP (StoreZip, TimeMonth);

16-9 ROLLUP Operator Example Output of query with ROLLUP operator StoreZipTimeMonthSumSales

16-10 GROUPING SETS Example SELECT StoreZip, TimeMonth, SUM(SalesDollar) AS SumSales FROM Sales, Store, Time WHERE Sales.StoreId = Store.StoreId AND Sales.TimeNo = Time.TimeNo AND (StoreNation = 'USA' OR StoreNation = 'Canada') AND TimeYear = 2005 GROUP BY GROUPING SETS((StoreZip, TimeMonth), StoreZip, TimeMonth, ());

16-11 GROUPING SETS Example Output of query with ROLLUP operator StoreZipTimeMonthSumSales

16-12 ROLAP Techniques  Bitmap join indexes  Star join optimization  Query rewriting  Summary storage advisors  Parallel query execution

16-13 Maintenance Workflow

16-14 Data Quality Problems  Multiple identifiers  Multiple field names  Different units  Missing values  Orphaned values  Multipurpose fields  Conflicting data  Different update times

16-15 ETL Tools  Extraction, Transformation, and Loading  Specification based  Eliminate custom coding  Third party and DBMS based tools

16-16 Refresh Processing