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Introduction To outline
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Data Warehousing Architecture
Extract Transform
Load Refresh
Serve
ExternalSources
Operational Dbs
Analysis
Query/Reporting
Data Mining
Monitoring &Administration
MetadataRepository
DATA SOURCES TOOLS
DATA MARTS
OLAP Servers
Reconciled data
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Online Analysis Processing(OLAP)
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.
Data Warehouse
Time
Product
Reg
ion
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Dimension and Member
DimensionDimension
MembersMembers
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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
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What is Essbase?
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.
Minute->Day->Week->Month->Qtr->YearProduct Line->Product Family->Product Cat->Product sub Cat
Minute->Day->Week->Month->Qtr->YearProduct Line->Product Family->Product Cat->Product sub Cat
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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
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Architecture
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Multidimensional Viewing and Analysis
Sales Slice of the Database
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Online Analysis Processing(OLAP)
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.
Data Warehouse
Time
Product
Reg
ion
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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.
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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
Dense and Sparse Dimensions
Blocks
Measures
Scen
ario
Year
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1
Block Numbering
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
11
33
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Rules Files
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.
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OLAP Operations
Drill Down
Time
Reg
ion
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
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OLAP Operations
Drill Up
Time
Reg
ion
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
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OLAP Operations
Slice and Dice
Time
Reg
ion
ProductProduct=Toaster
Time
Reg
ion
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OLAP Operations
Pivot
Time
Reg
ion
Product
RegionT
ime
Product
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Uses a cube metaphor to describe data storage.
An Essbase database is considered a “cube”, with each cube axis representing a different dimension, or slice of the data (accounts, time, products, etc.)
All possible data intersections are available to the user at a click of the mouse.
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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
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A Visual Operation: Pivot (Rotate)
1010
4747
3030
1212
Juice
Cola
Milk
Cream
NY
LA
SF
3/1 3/2 3/3 3/4
DateDate
Month
Reg
ion
Product
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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
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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.
Multidimensional Viewing and Analysis
Sales Slice of the Database
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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.
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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
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Multidimensional Viewing and Analysis
Data for January
Data for February
Data for Profit Margin
Data from Different Perspective
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