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.

1. Hyperion Demo
Presentation
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3. Raw Data
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Raw Data will be no use until it will become information

4. Raw Data -> Information
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How do you
find out the
profit of
Product
“Electronics”
from 100’ s of
Excel sheets
Metadata

5. Then what is OLTP
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
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6. Current Challenges
• I can’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
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7. What is Data Warehouse
 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
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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
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9. OLTP -> OLAP
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10. Why do you need the history
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 Study the past if you define the future

11. 11
Data Warehouse
Relational Detail Star Schemas
Common Dimensions Common Transformations
Data Models
GL Excel Sheets/Flat FilesHR
Dashboard Reporting

12. Data Mart
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Marketing
Mart
HR Data Mart
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
 Dimension tables establish the context of the facts
 In other words, Dimensional tables store fields that describe the facts
 Eg: Time Periods, Products, Customers etc
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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...;
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15. Slowly Changing Dimensions
 Type-I SCD (Over write)
 Type-II SCD (Maintain History)
 Type-III SCD(Alternate Realities)
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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
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17. Star Schema
 A single fact table and for each dimension one dimension table
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Fact Table
(or)
Measures
Time
Product Scenario
Customers

18. Snow Flake Schema
 Represent dimensional hierarchy directly
by normalizing tables.
 Gives more Detailed Information
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Fact Table
(or)
Measures
Time
Product Scenario
Countries Cities

19. Fact Constellation
 Multiple Fact Tables that share multiple dimensional tables
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Fact Table
(or)
Measures
Time
Product Scenario
Customers
Revenue

20. DWH Cycle
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Oracle
Flat
Files
DB2
Staging Area ETL
Enterprise DWH
DM1
DM3
DM2
OLAP
Business
Decision
Reports
Hyperion Resource
MDM / DRM

21. Dimensional Modeling Design Process
 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
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22. EPM
Enterprise Performance Management
A set 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
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23. 23

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
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25. Advantages of MOLAP
Hyperion is multi Slice Dice
dimensional database
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26. Drill-Down/Up
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Rollup

27. Essbase History
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Arbor Corporation Essbase
1992
Hyperion Solutions
1998
Essbase
Hyperion
Enterprise
Hyperion
Reporting
Planning and
Budgeting
Oracle Corporation Oracle EPM System
BI Foundation
Essbase
2007

28. Cube means
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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 tuned for Analysis
 Which customers are most profitable
 What is the customer likely to buy next
 What if demand falls short of forecast
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Why Essbase
• Richest business users experience
• Highly Advanced Calculation Engine
• Write-Back Capability Feature

30. Essbase Introduction
 Part of 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
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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
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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
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 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 Cubes Looks Like
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34. ESSBASE STUDIO
 Single graphical modeling environment and single setup for Essbase app
building and administration
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35. How business users Analyze Data
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36. Oracle EPM Workspace
 Single thin client environment bringing all of the EPM system and
BI tools together in one access point
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37. Integration with BI Tools – Smart View Addin
 Common add-in to provide integration with Microsoft office for oracle EPM
system and BI tools like Essbase, Planning, OBIEE, HFR
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38. User Security – Shared Services Console
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39. Life Cycle Management – Migration Tool
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40. Complete EPM System
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41. Life Cycle of Essbase
Database Objects
- Outline File
- Rule Files
- Calculation Scripts
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 Create an Application(ASO or BSO)
 Create an Database
Dimension Modeling
Data Loading
Report Generation
Hyperion Daily Maintenance Activities

42. Continuation
 Hyperion Essbase Installation
 Hyperion Services Order
 Essbase Log Files
 Essbase Applications Path
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Thankyou