Dataware Housing B.viswA VANI

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 1/14

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 2/14

2

Course Overview

0. IntroductionI. Data Warehousing

II. Decision Support

and OLAP

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 3/14

3

A producer wants to know….Which areour  lowest/highest m argincustom ers ?

Which are

our  lowest/highest m argincustom ers ?

Who are m ycustomersand what

productsare theybuying?

Who are mycustomersand what

productsare theybuying?

Whichcustomers

are most likelyto goto thecom petition ? 

Whic

hcustomers

are most likelyto goto thecom petition ? 

What impactwillnewproducts/services

have onrevenue

What impactwi

llnewproducts/services

have onrevenue

What productprom-

-otions havethe biggestimpact onrevenue?

What productprom-

-otions havethe biggestimpact onrevenue?

What is the

mosteffectivedistributionchannel?

What is themosteffectivedistributionchannel?

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 4/14

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 5/14

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 6/14

6

What is Data Warehousing?

A process of transforming data intoinformation and making

it available to users in atimely enough mannerto make a difference

[Forrester Research, April1996]

Data

Information

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 7/14

7

Evolution

60’s: Batch reportshard to find and analyze informationinflexible and expensive, reprogram every new

request

70’s: Terminal-based DSS and EIS (executiveinformation systems)still inflexible, not integrated with desktop tools

80’s: Desktop data access and analysis tools

query tools, spreadsheets, GUIseasier to use, but only access operational databases

90’s: Data warehousing with integrated OLAPengines and tools

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 8/14

8

Warehouses are Very LargeDatabases

35%

30%

25%

20%

15%

10%

5%

0%5GB

5-9GB

10-19GB 50-99GB 250-499GB

20-49GB 100-249GB 500GB-1TB

Initial

Projected 2Q96

Source: META Group, Inc.

      R    e     s     p      o     n

      d      e 

    n      t     s 

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 9/14

9

Very Large Data Bases

 Terabytes -- 10^12 bytes:

Petabytes -- 10^15 bytes:

Exabytes -- 10^18 bytes:

Zettabytes -- 10^21 bytes:

Zottabytes -- 10^24 bytes:

Walmart -- 24 Terabytes

Geographic InformationSystems

National Medical Records

Weather images

Intelligence AgencyVideos

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 10/14

10

Data Warehousing --It is a process

 Technique for assembling andmanaging data from varioussources for the purpose of 

answering businessquestions. Thus makingdecisions that were notprevious possible

A decision support databasemaintained separately fromthe organization’soperational database

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 11/14

11

Data WarehouseA data warehouse is a

subject-oriented

integrated

time-varyingnon-volatile

collection of data that is used

primarily in organizational decisionmaking. -- Bill Inmon, Building the Data Warehouse 1996

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 12/14

12

Data Warehouse Architecture

Data WarehouseEngine

Optimized Loader 

ExtractionCleansing

AnalyzeQuery

Metadata Repository

RelationalDatabases

LegacyData

PurchasedData

ERPSystems

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 13/14

Conclusion Technique for assembling and managing data

from various sources for the purpose of answering business questions.

8/9/2019 Dataware Housing B.viswA VANI

http://slidepdf.com/reader/full/dataware-housing-bviswa-vani 14/14

REFERENCEBuilding Data Warehouse by InmonData Mining:Concepts and Techniques by

Han,Kamber.

www.dwinfocenter.org

www.datawarehousingonline.com

www.billinmon.com