1. 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

2. 16-2 Data Comparison

3. 16-3 Applications

4. 16-4 Example: Star Schema

5. 16-5 Example: Input table records Sales Table SalesNoSalesDollorTimeNoStoreId 1100001011001 2120001021002 3110001031003 490001011004 5110001021005 6150001031006 TimeDim Table TimeNoTimeMonthTimeYear 10112005 10222005 10332005 10442006 Store Table StoreIdStoreStateStoreNationStoreZip 1001MNUSA80111 1002MNUSA80111 1003MNUSA80111 1004OHUSA80112 1005OHUSA80112 1006OHUSA80112

6. 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)

7. 16-7 CUBE Operator Example Output of query with CUBE operator StoreZipTimeMonthSumSales 80111110000 80111212000 80111311000 8011219000 80112211000 80112315000 80111 33000 80112 35000 119000 223000 326000 68000

8. 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);

9. 16-9 ROLLUP Operator Example Output of query with ROLLUP operator StoreZipTimeMonthSumSales 80111110000 80111212000 80111311000 8011219000 80112211000 80112315000 80111 33000 80112 35000 68000

10. 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, ());

11. 16-11 GROUPING SETS Example Output of query with ROLLUP operator StoreZipTimeMonthSumSales 80111110000 80111212000 80111311000 8011219000 80112211000 80112315000 80111 33000 80112 35000 119000 223000 326000 68000

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

13. 16-13 Maintenance Workflow

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

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

16. 16-16 Refresh Processing