This slide gives a glimpse about what is data warehouse.Will be very useful for beginners.

1. N. Jagadish Kumar
Assistant Professor
Velammal Institute of technology

2.  In today’s competitive global business environment,
understanding and managing enterprises wide
information is crucial for making timely decisions and
responding to changing business conditions.
 There is a tremendous amount of data generated by
day today business operational applications. Studies
indicate that the amount of data in a given
organizations doubles Every Five Years. so it is difficult
to analyse this data’s to make better decission.
 In order to solve this problem DATAWAREHOUSE as
emerged
INTRODUCTION TO DATA
WAREHOUSE

3. Datawarehousing is a popular and powerful concept of
applying information technology to turn this huge island
of data into meaningful information for better business
decisions.

4.  A Data warehouse is a subject
oriented, integrated ,time-varient
& non-volatile collection of data
in support of managements
decision making process.
 Bill Inmon designs Top-Down
approach
 A warehouse is a copy of
transaction data specifically
structured for query & analysis.
 Ralph Kimball designs Bottom-
Up approach
Bill Inmon
Ralph Kimball

6. oAn OLTP system is an application that modifies data
and has a large number of concurrent users.
oThis environment is the source for Data warehouse
which consist of current day to day data’s and it is
normalised.
LIST OF OPERATIONAL DATABASE:
Relational Database.
Eg: Oracle,SQL server,Sybase,Teradata
Files
CRM
ERP
External sources & Legacy systems.

7. Extract : Get the data out of the source systems
Transform : Convert the data into a useful format for
analysis.
Load : Get the data into the data warehouse
POPULAR ETL TOOLS ARE:
 Informatica
 Datastage
 Abinitio
 Oracle warehouse builder

8. OLAP is computer processing that enables a user to
easily and selectively extract and view data from different
points of view.
This environment helps the Business users to view the
Data’s in multidimensional format that makes the users
to take better decisions easily.
POPULAR OLAP TOOL ARE:
Cognos
Business objects
Micro Strategy
SAS
Crystal Report
Hyperion

9.  Generic Two-Level Architecture
 Independent Data Mart
 Dependent Data Mart and Operational
Data Store.

10. One company-
wide warehouse
Periodic extraction  data is not completely current in
warehouse

11. Data marts:Data marts:
Mini-warehouses, limited in scope
E
T
L
Separate ETL for each independent
data mart
Data access complexity due
to multiple data marts

12. ODSODS provides option for
obtaining current data
Single ETL for
enterprise data warehouse (EDW)(EDW)
E
T
L
Dependent data marts loaded
from EDW

14.  Data mining, the extraction of hidden predictive
information from large databases, is a powerful new
technology with great potential to help companies focus
on the most important information in their data
warehouses.
 Data Mining predicts future trends and behaviors,
allowing businesses to make proactive, knowledge driven
decisions.
Data mining is the process of analyzing business data in
the data warehouse to find unknown partners or rules of
information that you can use to tailor business
operations.

15. Data mining software is one of a number of
analytical tools for analyzing data. It allows
users to analyze data from many different
dimensions or angles, categorize it, and
summarize the relationships identified.

16. Clustering - is the task of discovering groups and
structures in the data that are in some way or another
"similar", without using known structures in the data.
Classification - is the task of generalizing known
structure to apply to new data.
Regression - Attempts to find a function which
models the data with the least error.
Association rule learning - Searches for
relationships between variables.

17. Artificial neural networks: Non-linear predictive models
that learn through training and resemble biological neural
networks in structure.
Genetic algorithms: Optimization techniques that use
processes such as genetic combination, mutation, and natural
selection in a design based on the concepts of natural
evolution.
Decision trees: Tree-shaped structures that represent sets of
decisions.
Nearest neighbor method: A technique that classifies each
record in a dataset based on a combination of the classes of the
k record(s) most similar to it in a historical dataset (where k 1).
Rule induction: The extraction of useful if-then rules from
data based on statistical significance.
Data visualization: The visual interpretation of complex
relationships in multidimensional data. Graphics tools are
used to illustrate data relationships.

18. The data warehouse is the hub for decision support data.
A good data warehouse will provide the RIGHT data to
the RIGHT people at the RIGHT time: RIGHT NOW!
So customers can use data warehouses to improve their
decision making and their competitive advantage
Data warehouse also plays a major role in DATA
MINING to predict future trends and behaviours and
knowledge Driven Decision