Basic Introduction of Data Warehousing from Adiva Consulting

Hyperion Essbase & Planning Training
www.adivaconsulting.com1

www.adivaconsulting.com
Raw Data
2
Raw Data will be no use until it will become information

Raw Data -> Information
3
How do you
find out the
profit of
Product
“Electronics
” from 100’
s of Excel
sheets
Metadata

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
4

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
5

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
6

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
7

OLTP -> OLAP
8

Why do you need the history
9
 Study the past if you define the future

Data Warehouse
Relational Detail Star Schemas
Common Dimensions Common Transformations
Data Models
GL
Excel Sheets/Flat
Files
HR
Dashboard Reporting

Data Mart
11
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.

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
12
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

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...;
13

Slowly Changing Dimensions
• Type-I SCD (Over write)
• Type-II SCD (Maintain History)
• Type-III SCD(Alternate Realities)
Cust ID Cust Name Cust City
10 XYZ New York
10 XYZ Seattle
Change of Attributes
No History
Maintained
Cust ID Cust Name Cust City Date
10 XYZ New York 1-Jan-2000
ALL History
Maintained
Cust ID Cust Name Cust City Date
10 XYZ New York 1-Jan-2000
10 XYZ Seattle 1-Jan-2005
10 XYZ New York
Cust ID Cust Name Cust City1 Cust City 2
10 XYZ New York Seattle
History In
Separate
columns
14

Schema Design
Schema Types
 Star Schema
Snow-Flake Schema
Fact Constellation schema or Galaxy Schema
15

Star Schema
• A single fact table and for each dimension one
dimension table
16
Fact Table
(or)
Measures
Time
Product Scenario
Customers

Snow Flake Schema
• Represent dimensional hierarchy directly
by normalizing tables.
• Gives more Detailed Information
17
Fact Table
(or)
Measures
Time
Product Scenario
Countries Cities

Fact Constellation
• Multiple Fact Tables that share multiple
dimensional tables
18
Fact Table
(or)
Measures
Time
Product Scenario
Customer
s
Revenue

DWH Cycle
19
Oracl
e
Flat
Files
DB2
Staging
Area ETL Enterprise
DWH
DM1
DM3
DM2
OLAP
Business
Decision
Reports
Hyperion
Resource
MDM /
DRM

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
20

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
21

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
23

Advantages of MOLAP
Hyperion is multi Slice Dice
dimensional database
24

Drill-Down/Up
25
Rollup

Essbase History
Arbor
Corporation
Essbase
1992
Hyperion
Solutions
1998
Essbase
Hyperion
Enterprise
Hyperion
Reporting
Planning and
Budgeting
Oracle
Corporation
Oracle EPM System
BI Foundation
Essbase
2007
26

Cube means
27
Intersecting Dimensions
-- Form Data Cells
OLAP Storage Paradigm
-- Multidimensional
databases are
array structures , not
related tables
-- Will concentrate about
cells not fields

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
28
Why Essbase
• Richest business users experience
• Highly Advanced Calculation Engine
• Write-Back Capability Feature

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
Essbase
Planning
&
Budgeting
Forecas
ting
Product
Analysis
Customer
Analysis
Essbase Usage
Minute->Day->Week->Month->Qtr->Year
Product Line->Product Family->Product Cat-
>Product sub Cat
29

Essbase Architecture
30
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

How Essbase Thinks
31
 Multidimensional Cubes
 Dimensions
 Common grouping of
master data
like Organization , Products,
Accounts
Optimized Data Storage
 Block Storage
 Aggregate Storage
 XOLAP
 Drill Through Reporting

How Essbase Cubes Looks Like
32

ESSBASE STUDIO
• Single graphical modeling environment and single setup for
Essbase app building and administration
33

How business users Analyze Data
34

Oracle EPM Workspace
• Single thin client environment bringing all of the EPM
system and BI tools together in one access point
35

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
36

User Security – Shared Services Console
37

Life Cycle Management – Migration Tool
38

Complete EPM System
39

Life Cycle of Essbase
Database Objects
- Outline File
- Rule Files
- Calculation Scripts
40
 Create an Application(ASO or BSO)
 Create an Database
Dimension Modeling
Data Loading
Report Generation
Hyperion Daily Maintenance Activities

Continuation
• Hyperion Essbase Installation
• Hyperion Services Order
• Essbase Log Files
• Essbase Applications Path
41

Thank you

Basic Introduction of Data Warehousing from Adiva Consulting

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Basic Introduction of Data Warehousing from Adiva Consulting