The document discusses a seminar on data warehousing presented by Sangram Keshari Swain. It defines a data warehouse as a subject-oriented, integrated, non-volatile collection of data used to support management decision making. The primary concept is separating nonvolatile data for analysis from operational systems. A data warehouse provides a single view of enterprise data optimized for reporting and analysis through extracting and integrating data from different sources.

1. SEMINAR ON
DATA
WAREHOUSING

2. GUIDED BY :
DR. JIBITESH MISHRA SIR
PRESENTED BY :
SANGRAM KESHARI SWAIN
ADMN.NO. – 14MCA/02
M C A 2nd YR. ( 4th SEM.)

3. INTRODUCTION :
In 1992 W.H.INMON characterized
a data warehouse as “a subject oriented,
integrated , non-volatile , time variant
collection of data in support of
management’s decision”. Data warehouse
provide access to data for complex analysis,
knowledge discovery and decision making.

4. Data Warehousing
The primary concept behind
data warehousing is that the
nonvolatile data stored for
business analysis can be most
effectively managed by separating
it from the active data in the
operational systems. Nonvolatile
data is data that is not modified or
rarely modified after being moved
from operational systems to a data
warehouse.

5. Data Warehousing (Data Warehouse)
It is a single view of your enterprise data
- optimized for reporting and analysis
-copy of transaction and non-transaction data.
-data and information are extracted from hetero-
geneous production data sources as they are
generated, or in periodic stages, making it simpler and
more efficient to run queries over data that originally
came from different sources.
-Interactive content can be delivered to anyone in the
extended enterprise – customers, partners, employees,
managers, and executives – anytime, anywhere.

7. FUNDAMENTAL :
IS
│
┌───────────┐
OLTP DW

8. Overal process of DW
Cleaning reforming
Meta data
data
Back flushing
Other data inputs
DATABASE
Updates/new data
OLAP
DSSI
ESI

9. OLTP :
Traditional data base support
on-line transaction process (OLTP) ,
which includes insertion , updates, and
deletion while also supporting information
query requirements. That may touch a
small part of the data base and transactions
that deal with insertion or updates of a few
topples per relation to process. Thus they can
not be optimized for OLAP, DSS, or DATA
MINING .

10. DATA WAREHOUSE
TERMINOLOGY AND DEFINATION
A data warehouse is…
- the data ( i.e meta ,fact, dimension,
aggregation)
- and the process managers ( load , ware
houses ,query) that make information
available for taking informed decisions .
- it is designed for query and analysis rather
than transaction processing .

11. What is OLAP :
OLAP ( on-line analytical processing ) is
used to describe the analysis of complex
data for the data warehouse. In the hand
of skilled knowledge workers,OLAP tools
used distributed computing capabilities for
analysis that require more storage and
processing power that can be economica-
lly and efficiently located an individual
desktop .

12. WHAT IS DSS :
DSS ( decision support system ) is
also known as EIS ( executive
information system ). It supports an
organization's leading decision
makers with higher level data for
complex and important decisions.

13. DATA MINING :
By contrast , data warehouse are
designed précised to support efficient
extraction processing and presentation
for analytic and decision making
purpose . In comparison to traditional
database data warehouses generally
contain very large amounts of data from
multiple process that may include
database from different data models
and sometimes file acquired from
independent system and platform .

14. INMON’S CHARACTERISTICS :
 SUBJECT ORIENTED : Focus on a
topic to analyze data.
 INTEGRATED : Data from disparate
sources must be put into a consistent
format .
 NON-VOLATILE : Data should not
change once into the warehouse.

15. DATA WAREHOUSE
ARCHITECHTURE

16. COMPONENTS OF DATA
WAREHOUSE
 WHEN AND HOW TO GATHER DATA :
In a source-driven architecture for
gathering data ,the data sources
transmit new information ,either
continually or periodically . In
estination-driven architecture, the
data warehouse periodically sends
request for new data to the sources

17. * WHAT SCHEMA TO USE :
Data sources that have been
constructed independently are likely to
have different schema .In fact they
may even use different data models.
Part of task of a warehouse is to
perform schema integration, and to
convert data to the integrated schema
before they are stored. As a result the
data stored in the warehouse are not
just a copy of the data of the data at
the sources.

18.  DATA CLEANSING :
- The task of correcting and pre-processing
data is called data cleaning.
-Records for multiple individuals in a house
may grouped together so only mailing is sent
to each house (house holding).
 HOW TO PROPAGATE DATA :
Updates on relations at the sources must
be propagated to the warehouse .If the
relation at the data warehouse are exactly
same as those data sources ,the propagation
is straight forward. If they are not ,the
problem of propagating updates is basically
the view-maintenance problem.

19.  WHAT DATA TO BE SUMMERISE :
The raw data generated by a
transaction process system may be too
large to store on-line. However we can
answer many queries by maintaining
just summery data obtained
aggregation on a relation rather than
maintaining the entire relation .

20. DATE
MONTH
QUARTER
YEAR
ITEM-ID
ITEM NAME
COLOR
SIZE
CATEGORY ITEM-ID
STORE-ID
CUSTOMER-ID
DATE
NUMBER
PRICE
STORE-ID
CITY
STATE
COUNTRY
CUSTOMER-ID
NAME
STREET
CITY
STATE
COUNTRY
STAR SCHEMA FOR A DATA WAREHOUSE
Item info
store
Date info
customer

21. Data Warehousing with NGS-IQ

22. SUMMARY
Data warehouse is latest technology.
It helps gather and archive
operational data. Warehouse are used
for decision supportand analysis of
historical data,for instance to predicts
trends.