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Expert talk
Expert talk
Expert talk
Expert talk
Expert talk
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Expert talk
Expert talk
Expert talk
Expert talk
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Expert talk
Expert talk
Expert talk
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Expert talk

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  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
  3. Online Analysis Processing(OLAP) <ul><li>It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. </li></ul>Data Warehouse Time Product Region
  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
  6. What is Essbase? <ul><li>It is a multidimensional database that enables Business Users to analyze Business data in multiple views/prospective and at different consolidation levels . It stores the data in a multi dimensional array . </li></ul>Minute->Day->Week->Month->Qtr->Year Product Line->Product Family->Product Cat->Product sub Cat
  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                                                                                   
  10. Online Analysis Processing(OLAP) <ul><li>It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. </li></ul>Data Warehouse Time Product Region
  11. <ul><li>Generation:- Generation refers to a consolidation level within a dimension. A root branch of the tree is generation 1. Generation numbers increase as you count from the root toward the leaf member. </li></ul>
  12.  
  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
  15. Rules Files <ul><li>Rules define operations that Essbase performs on data values or on dimensions and members when it processes a data source. Use rules to map data values to an Essbase database or to map dimensions and members to an Essbase outline. </li></ul>
  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
  20. <ul><li>Uses a cube metaphor to describe data storage. </li></ul><ul><li>An Essbase database is considered a “cube”, with each cube axis representing a different dimension , or slice of the data (accounts, time, products, etc.) </li></ul><ul><li>All possible data intersections are available to the user at a click of the mouse. </li></ul>
  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

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