The document discusses data warehousing architecture and multidimensional databases. It describes how data is extracted, transformed and loaded into a data warehouse for analysis using online analytical processing (OLAP). Essbase is introduced as a multidimensional database that allows users to analyze business data from multiple views and consolidation levels by storing data in a multidimensional array. Common OLAP operations like drill down, drill up, slice, dice and pivot are also outlined.

1. Introduction To outline

2. Data Warehousing Architecture Extract Transform Load Refresh Serve External Sources Operational Dbs Analysis Query/Reporting Data Mining Monitoring & Administration Metadata Repository DATA SOURCES TOOLS DATA MARTS OLAP Servers Reconciled data

4. Dimension and Member Dimension Members

5. Hyperion Product Suite Hyperion Hyperion BI+ Reporting Hyperion BI+ Application Hyperion BI+ Data Management HFM (Hyperion Financial Management) HSF (Hyperion Strategic Financial) Hyperion Planning HPM (Hyperion Performance Management) MDM (Maser Data Management) FDQM (Financial Query Data Management) HAL (Hyperion Application Link) DIM (Data Integrated Management) Hyperion Essbase Analyzer Reports Interacting Reports Production Reporting

7. Oravision Oracle Online Training/Consultancy Solution aloo_a2@yahoo.com Essbase Multi Dimension Data Modeling (Complete Life Cycle) Physical Data Model Physical Tables from ODS Environment Logical Multi Dimensional Model Multi Dimensional View Presentation Layer Reporting

8. Architecture

9. Multidimensional Viewing and Analysis Sales Slice of the Database

13. Dense and Sparse Dimensions Index: 100-10, New York 100-20, New York 100-30, New York 200-10, New York 200-20, New York 200-30, New York Database: Basic Year Measure Scenario Product Market Blocks Measures Scenario Year

14. Block Numbering 1 Index: 100-10, New York 100-20, New York 100-30, New York 100, New York 200-20, New York 200-30, New York 200-40, New York 200, New York . . . 100-10, Massachusetts 100-20, Massachusetts 100-30, Massachusetts . . . 2 4 5 6 20 21 7 22 8 33 1 3

16. OLAP Operations Drill Down Time Region Product Category e.g Electrical Appliance Sub Category e.g Kitchen Product e.g Toaster

17. OLAP Operations Drill Up Time Region Product Category e.g Electrical Appliance Sub Category e.g Kitchen Product e.g Toaster

18. OLAP Operations Slice and Dice Time Region Product Product=Toaster Time Region

19. OLAP Operations Pivot Time Region Product Region Time Product

21. Multidimensional Data 10 47 30 12 Juice Cola Milk Cream NY LA SF Sales Volume as a function of time, city and product 3/1 3/2 3/3 3/4 Date

22. A Visual Operation: Pivot (Rotate) 10 47 30 12 Juice Cola Milk Cream NY LA SF 3/1 3/2 3/3 3/4 Date Month Region Product

23. Multidimensional Viewing and Analysis Consider the three dimensions in a databases as Accounts, Time, and Scenario where Accounts has 4 members, Time has 4 members and Scenario has two members. Three-Dimensional Database

24. Multidimensional Viewing and Analysis The shaded cells is called a slice illustrate that, when you refer to Sales, you are referring to the portion of the database containing eight Sales values. Sales Slice of the Database

25. Multidimensional Viewing and Analysis Actual, Sales Slice of the Database When you refer to Actual Sales, you are referring to the four Sales values where Actual and Sales intersect as shown by the shaded area.

26. Multidimensional Viewing and Analysis Data value is stored in a single cell in the database. To refer to a specific data value in a multidimensional database, you specify its member on each dimension. The cell containing the data value for Sales, Jan, Actual is shaded. The data value can also be expressed using the cross-dimensional operator (->) as Sales -> Actual -> Jan. Sales -> Jan -> Actual Slice of the Database

27. Multidimensional Viewing and Analysis Data for January Data for February Data for Profit Margin Data from Different Perspective