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.
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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10. Why do you need the history
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Study the past if you define the future
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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
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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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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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
34. ESSBASE STUDIO
Single graphical modeling environment and single setup for Essbase app
building and administration
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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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