basic introduction of data warehousing from adiva consulting

Hyperion Essbase & Planning Training

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BASIC INTRODUCTION OF DATA WAREHOUSING

www.adivaconsulting.com

Raw Data

Raw Data will be no use until it will become information

Raw Data -> Information

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

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

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

In Other Words

• A data warehouse is a subject-orientedIntegrated time-varying non-volatile collection of data that is used primarily in organizational decision making.--------Bill Inmon

OLTP -> OLAP

Why do you need the history

Study the past if you define the future

Data WarehouseRelational Detail Star SchemasCommon Dimensions Common TransformationsData Models

GL Excel Sheets/Flat FilesHR

Dashboard Reporting

Data Mart

MarketingMart

HR Data Mart

Sales Data Mart

Data Marts

DataWarehouse

Data grouped for a specific subject area and considered as subset of data warehouseCan 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

Fact TableFact 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...;

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 SeattleChange of Attributes

No History Maintained

Cust ID Cust Name Cust City Date

10 XYZ New York 1-Jan-2000Change 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

10 XYZ New York

Cust ID Cust Name Cust City1 Cust City 2

10 XYZ New York SeattleChange of Attributes

History In Separate columns

Schema Design

Schema Types Star SchemaSnow-Flake SchemaFact Constellation schema or Galaxy Schema

Star Schema

• A single fact table and for each dimension one dimension table

Fact Table (or)

Measures

Product Scenario

Customers

Snow Flake Schema

• Represent dimensional hierarchy directly by normalizing tables.• Gives more Detailed Information

Fact Table (or)

Measures

Product Scenario

Countries Cities

Fact Constellation

• Multiple Fact Tables that share multiple dimensional tables

Fact Table (or)

Measures

Product Scenario

Customers

Revenue

DWH Cycle

Oracle

Flat Files

Staging Area ETL Enterprise

DM2OLAP

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

Enterprise Performance ManagementA 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

Basic Introduction of Hyperion Essbase

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

Advantages of MOLAP

Hyperion is multi Slice Dice

dimensional database

Drill-Down/Up

Rollup

Essbase History

Arbor Corporation Essbase

Hyperion Solutions

EssbaseHyperion Enterprise

Hyperion Reporting

Planning and Budgeting

Oracle Corporation

Oracle EPM System

BI Foundation

Essbase2007

Cube means

Intersecting Dimensions -- Form Data CellsOLAP 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

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

Forecasting

Product Analysis

Customer Analysis

Essbase Usage

Minute->Day->Week->Month->Qtr->YearProduct Line->Product Family->Product Cat->Product sub Cat

Essbase Architecture

Essbase Server

Essbase Database

Provider Services

Smart-View

Essbase Excel-Add-in, MaxL ,

TCP/IP

TCP/IP HTTP

Administration Services

EssbaseStudio ServicesRDMS

TCP/IP

EAS Console

Essbase StudioConsole

Database Tier

Middle Tier

Client Tier

How Essbase Thinks

Multidimensional Cubes Dimensions Common grouping of master data like Organization , Products, AccountsOptimized Data Storage Block Storage Aggregate Storage XOLAP Drill Through Reporting

How Essbase Cubes Looks Like

ESSBASE STUDIO• Single graphical modeling environment and single setup for

Essbase app building and administration

How business users Analyze Data

Oracle EPM Workspace

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

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

User Security – Shared Services Console

Life Cycle Management – Migration Tool

Complete EPM System

Life Cycle of Essbase

Database Objects- Outline File- Rule Files- Calculation Scripts

Create an Application(ASO or BSO) Create an DatabaseDimension ModelingData LoadingReport GenerationHyperion Daily Maintenance Activities

Continuation

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

Thank you

basic introduction of data warehousing from adiva consulting

available data

atomic data

summarized data

raw data information

data warehouse relational

subset of data warehouse

consistent store of

unexpected data needs

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