Expert talk

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Introduction To outline


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


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


Dimension and Member

DimensionDimension

MembersMembers


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


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


Architecture


Multidimensional Viewing and Analysis

Sales Slice of the Database


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


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.


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


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



. . .

2

4

5

6

20

21

7

22

8

33

11

33


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.


OLAP Operations

Drill Down

Time

Reg

ion

Product

Category e.g Electrical Appliance

Sub Category e.g Kitchen

Product e.g Toaster


OLAP Operations

Drill Up

Time

Reg

ion

Product

Category e.g Electrical Appliance

Sub Category e.g Kitchen

Product e.g Toaster


OLAP Operations

Slice and Dice

Time

Reg

ion

ProductProduct=Toaster

Time

Reg

ion


OLAP Operations

Pivot

Time

Reg

ion

Product

RegionT

ime

Product


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.


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


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



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


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



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.



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



Data for January

Data for February

Data for Profit Margin

Data from Different Perspective

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