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Oracle Hyperion overview
This document provides an overview of Hyperion and Essbase. It discusses how raw data is transformed into information through data warehousing processes like extracting, transforming, and loading data. It then explains what an OLTP system is and how Essbase provides multi-dimensional analysis capabilities. Key features of Essbase like dimensions, facts, aggregation, and its architecture are summarized. Finally, the document outlines the typical lifecycle of building and maintaining an Essbase database application.
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Oracle Hyperion overview
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- 3. Raw Data 3 Raw Datawill be no use until it will become information
- 4. Raw Data ->Information 4 How do you find out the profit of Product “Electronics” from 100’ s of Excel sheets Metadata
- 5. Then what isOLTP In general, All Database Systems are OLTP Most RDBMS systems are OLTP Detailed, Up to Date Data Read/Update of few records Run the business in real time Historical Data will be archived for performance reasons Eg: Walk into Reliance Store you will find OLTP Walk into ATM you will find OLTP Buy TV in electronic shops Buy Stocks in Broker like Etrade -> OLTP 5
- 6. Current Challenges • Ican’t find the data I need – data is scattered over the network – many versions, subtle differences – No Single source for Information • I cant understand the data I found – available data poorly documented • I can’t use the data I found – results are unexpected – data needs to be transformed from one form to other What's certain about today's business climate is uncertainty 6
- 7. What is DataWarehouse A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. - Barry Delvin 7
- 8. In Other Words A data warehouse is a subject-oriented Integrated time-varying non-volatile collection of data that is used primarily in organizational decision making. --------Bill Inmon 8
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- 10. Why do youneed the history 10 Study the past if you define the future
- 11. 11 Data Warehouse Relational DetailStar Schemas Common Dimensions Common Transformations Data Models GL Excel Sheets/Flat FilesHR Dashboard Reporting
- 12. Data Mart 12 Marketing Mart HR DataMart Sales Data Mart Data Marts DataWarehouse Data grouped for a specific subject area and considered as subset of data warehouse Can contain atomic data and summarized data. Generally Each data mart is designed for each department like Marketing, Sales etc.
- 13. Dimension Tables Dimensiontables establish the context of the facts In other words, Dimensional tables store fields that describe the facts Eg: Time Periods, Products, Customers etc 13 Fact Table Fact tables are used to record actual facts or measures in the business. Facts are the numeric data items that are of interest to the business Access via dimensions
- 14. Types of Measures-Facts Additive: Valid to SUM up to any Dimensional level -SUM(Sales_Amount) Semi-Additive: Semi-Additive measures are measures that can be added across some, but not all dimensions. For example the bank account balance is simply a snapshot in time and cannot be summed over time. -Sum(balance) where month=2011-12-12 Non-Additive=never used in a Sum Eg: Gross-Margin , Ratios etc...; 14
- 15. Slowly Changing Dimensions Type-I SCD (Over write) Type-II SCD (Maintain History) Type-III SCD(Alternate Realities) info@leadinnovativetechnologies.com 15 Cust ID Cust Name Cust City 10 XYZ New York Cust ID Cust Name Cust City 10 XYZ Seattle Change of Attributes No History Maintained Cust ID Cust Name Cust City Date 10 XYZ New York 1-Jan-2000 Change of Attributes ALL History Maintained Cust ID Cust Name Cust City Date 10 XYZ New York 1-Jan-2000 10 XYZ Seattle 1-Jan-2005 Cust ID Cust Name Cust City 10 XYZ New York Cust ID Cust Name Cust City1 Cust City 2 10 XYZ New York Seattle Change of Attributes History In Separate columns
- 16. Schema Design Schema Types Star Schema Snow-Flake Schema Fact Constellation schema or Galaxy Schema 16
- 17. Star Schema Asingle fact table and for each dimension one dimension table 17 Fact Table (or) Measures Time Product Scenario Customers
- 18. Snow Flake Schema Represent dimensional hierarchy directly by normalizing tables. Gives more Detailed Information info@leadinnovativetechnologies.com 18 Fact Table (or) Measures Time Product Scenario Countries Cities
- 19. Fact Constellation MultipleFact Tables that share multiple dimensional tables 19 Fact Table (or) Measures Time Product Scenario Customers Revenue
- 20. DWH Cycle 20 Oracle Flat Files DB2 Staging AreaETL Enterprise DWH DM1 DM3 DM2 OLAP Business Decision Reports Hyperion Resource MDM / DRM
- 21. Dimensional Modeling DesignProcess Choose a business process to model - Business activity that is valuable to analyze -Set of transactions that can be collected in a fact table Declare the Grain of the fact table -level of detail that you will record in the fact table Choose the Dimensions -Descriptive information about transactions -Usually want to limit number of dimensions Choose the Metrics -Numeric fields tagged to each fact table row 21
- 22. EPM Enterprise Performance Management Aset of processes that help organizations optimize their business performance. It is a framework for organizing, automating and analyzing business methodologies , metrics, processes and systems that drive business performance The products formerly known as Hyperion provide Enterprise Performance Management ("EPM") capabilities 22
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- 24. Multi Dimensional Analysis Query tool caches pre-computed aggregates in memory or on mid-tier server for extra-fast response time. Used to Analyze the future business based on past and present sales Eg: Sales Analysis Avoid spending time in analyzing huge numbers of daily transactions data Essbase stands for Extended Spreadsheet Analysis Used to Analyze data in multiple view of perspective so that business users can take decision for forecast analysis 24
- 25. Advantages of MOLAP Hyperionis multi Slice Dice dimensional database 25
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- 27. Essbase History 27 Arbor CorporationEssbase 1992 Hyperion Solutions 1998 Essbase Hyperion Enterprise Hyperion Reporting Planning and Budgeting Oracle Corporation Oracle EPM System BI Foundation Essbase 2007
- 28. Cube means 28 Intersecting Dimensions --Form Data Cells OLAP Storage Paradigm -- Multidimensional databases are array structures , not related tables -- Will concentrate about cells not fields
- 29. Essbase is tunedfor Analysis Which customers are most profitable What is the customer likely to buy next What if demand falls short of forecast 29 Why Essbase • Richest business users experience • Highly Advanced Calculation Engine • Write-Back Capability Feature
- 30. Essbase Introduction Partof Business Intelligence Foundation in Oracle EPM System widely considered to be the industry leading OLAP (On-Line Analytical Processing) server 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 30 Essbase Planning & Budgeting Forecasti ng Product Analysis Customer Analysis Essbase Usage Minute->Day->Week->Month->Qtr->Year Product Line->Product Family->Product Cat->Product sub Cat
- 31. Essbase Architecture 31 Essbase Server Essbase Database Provider Services Smart-View Essbase Excel-Add-in, MaxL, MDX TCP/IP TCP/IP HTTP Administration Services Essbase Studio ServicesRDMS ODBC A B D E C F A B D E C F TCP/IP HTTP EAS Console Essbase Studio Console Database Tier Middle Tier Client Tier
- 32. How Essbase Thinks 32 Multidimensional Cubes Dimensions Common grouping of master data like Organization , Products, Accounts Optimized Data Storage Block Storage Aggregate Storage XOLAP Drill Through Reporting
- 33. How Essbase CubesLooks Like 33
- 34. ESSBASE STUDIO Singlegraphical modeling environment and single setup for Essbase app building and administration info@leadinnovativetechnologies.com 34
- 35. How business usersAnalyze Data 35
- 36. Oracle EPM Workspace Single thin client environment bringing all of the EPM system and BI tools together in one access point info@leadinnovativetechnologies.com 36
- 37. Integration with BITools – Smart View Addin Common add-in to provide integration with Microsoft office for oracle EPM system and BI tools like Essbase, Planning, OBIEE, HFR 37
- 38. User Security –Shared Services Console info@leadinnovativetechnologies.com 38
- 39. Life Cycle Management– Migration Tool info@leadinnovativetechnologies.com 39
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- 41. Life Cycle ofEssbase Database Objects - Outline File - Rule Files - Calculation Scripts 41 Create an Application(ASO or BSO) Create an Database Dimension Modeling Data Loading Report Generation Hyperion Daily Maintenance Activities
- 42. Continuation Hyperion EssbaseInstallation Hyperion Services Order Essbase Log Files Essbase Applications Path 42
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