ETL Tool

E – Extract 2setting the data

T-transform2doing Intermediate operation

L-Load2Load to destination



Data stage, Informatica, AB-Initio, SSIS, OWB et


Analytical tool

To create multi – Dimensional object and object and provides supports for multi –Dimensional analysis



 COGNOS, BO, Hyperion, SSAS, Micro strategy, OBIEE etc.


Reporting tools

To display the data in the understand and required format, we go for this,



COGNOS, BO, SSRS, Hyperion, Micro strategy B Crstal on  OBIEE etc.


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It is composed on observable and recordable facts, that are off be found in transaction(or)operation system



 It is an integrated collection of facts and  is used as the[organized – data is called information] bases for decision making


Need of dataware housing

To have single version of data, in single storage

To complete with other organization market

To analysis tactical strategic queries etc.

Definition of ODS, DWH, BI, OLTP, OLAP


 On line transaction processing

Here day to day operations data available (immediate operation data) Here all the DML operation can be performed.

ODS(Operational data store)

It is data stag structure that acts like repository for near real time operational data rather than long trand data.

This ODS may for them become to enterprise operational database(or) stage area to data were house

DWH(Data were house)

It is data base structure, where huge data store, so that we can take appropriate decision

OLAP(Online Analytical processing)

It is an application that collects managing and present multi– dimensional data analysis and management purpose.

BI(Business Intelligence)

BI retrieve the data from data warehouse to make business decision and management(it can takes the  help OLAP)

Data warehouse Definition

There are many authors founder define different approaches





Some people calling INMON father of data ware house

A Data ware house is subject oriented


Time variant

Non-volatile, Data Granularity

Collection or data, in this support of

Decision making


A Data warehouse is subject oriented, integrated, Non- volatile, time variant, separate collection of data


Subject Oriented

The data in data ware house should belong to a specific subject Area, Domain (or) Business



Entire banking, Information in single place Entire medias information in single place



By loading the data we need follows standardized mechanism, we have single management data



Naming conversion, coding measurement, data attribute etc.


Non – volatile

Data in the data were house data, should not updata and delete.


Time variant

The data should be store based on time frame so that, we cab current month sales, what is


Previous month

sales, what could be sales of future month

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 Where  have should be separate traditional system



Where have should be high available etc.


Data Warehousing life cycle

It is combination of 3 life cycles creating a data base and modeling database.

ETL requirement gathering

ETL Analysis

ETL Design

ETL Coding

ETL Testing

ETL deployment and maintenance

Analytical life cycle

Requirement gathering analysis

Designing the objects

Create multi-dimensional object

Testing multi- dimensional object

Deployment implement

Reporting life cycle

Reporting Testing

Reporting Design

Reporting Coding

Reporting Publishers And maintenance.

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