This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.

1. DATA
WAREHOUSING
PRESENTED BY:-
SHRUTI DALELA
MCA V SEM

2. OUTLINES:-
 WHAT IS A DATA WAREHOUSING
 DATA WAREHOUSING DEFINITION
 HISTORY OF DATA WAREHOUSING
 FACTS ABOUT DATA WAREHOUSING
 CHARACTERISTICS OF DATA
WAREHOUSING
 USAGE AND TRENDS
 ARCHITECTURE OF DATA WAREHOUSE
 DBMS VS DATA WAREHOUSE

3. What Is A Data Warehouse?
A data warehouse is a powerful database
model
that significantly enhances the user’s ability
to
quickly analyze large, multidimensional data
sets.
It cleanses and organizes data to allow users
to
make business decisions based on facts.
Hence,
the data in the data warehouse must have

4. Data Warehousing Definition:-
 Date warehousing is an aspect to gather data
from multiple sources into central
repository,called Data warehouse.
 According to William H.Inmon,a leading
architect in the construction of data warehouse
systems,”A data warehouse is a subject –
oriented ,integrated ,time variant and non-
volatile collection of data in support of
management’s decision making process.
 “A data warehouse is simply a single complete,
and consistent store of data obtained from a
variety of sources and made available to end
users in a way they can understand and use it
in a business context.”
-----Barry Devlin,IBM consultant

5. DATA WAREHOUSES
Data Warehouses:
 Data spread in several databases –
physically located at numerous sites
 Data warehouse – repository of multiple
DBs in single schema; resides at single site.
 Data warehousing processes
1. Data Cleaning 2. Data Integration 3. Data
Transformation
4. Data Loading 5. Periodic data refreshing

6. Data warehouse diagram

7.  Data cleaning:-Data Cleaning includes,
filling in missing values, smoothing noisy
data, identifying or removing outliers, and
resolving inconsistencies.
 Data integration:-Data Integration includes
integration of multiple databases, data
cubes, or files.
 Data transformation:-Convert data from
legacy or host format to warehouse format.
 Load :-sort;summarize,consolidate;compute
views; check integrity.Build indices and
partitions.
 Refresh:-Propagates the update from data
sources to the warehouse.

9.  Data in a data warehouse are organized
around major subjects.
 Data provide information on historical
perspective – summarized on periodic
dimension.
 Eg. Sales of an item for a region in a period
 Data warehouse model – multidimensional
database structure / data cube
 Dimensions – Attributes / set of attributes
 Facts – Aggregated measures (Count /
Sales amount)

10. History of data warehousing
 The concept of data warehousing
dates back to the late 1980s when
IBM researcher Barry davlin and paul
murphy developed the “the business
data warehouse”.
 In essence, the data warehousing
concept was intended to provide an
architectural model for the flow of data
from opeational systems to decision
support environments.

11. Facts about data
warehousing:-
 Issues involved in warehousing include
techniques for dealing with errors and
techniques for efficient storage and
indexing of large volumes of data.
 This system is used for reporting and data
analysis.
 It usually contains historical data derived
from transaction data.
 Data warehousing is not meant for current
“live”data.

12. Components of a data
warehouse
• Sources –Data source interaction
• Data Transformation
• Data warehouse (data storage )
• Reporting (Data presentation )
• Metadata

14. Complete control over the four main
areas
of data management systems: -
 Clean data
 Query processing: multiple options
 Indexes: multiple types
 Security: data and access
Data Warehouse Advantages

15. Data Warehousing
Disadvantages
 Adding new data sources takes time and
associated high cost.
 Data owners lose control over their data,
raising ownership, security and privacy
issues.
 Long initial implementation time and
associated high cost.
 Difficult to accommodate changes in data
types and ranges, data source schema,
indexes and queries.

16. Characteristics of Data
Warehousing:-
 Subject –Oriented:-A data warehouse can
be used to analyze a particular subject
area.
For example:-”sales” can be a particular
subject.
 Integrated:-A data warehouse integrates
data from multiple data sources.
For example:-Source A and source B may
have different ways of identifying a product,
but in a data warehouse,there will be only
a single way of identifying a product.
 Time Variant :-Historical data is kept in a
data warehouse.

17. For example:-One can retrieve data from 3
months ,6months, 12 months ,or even older
data from a data warehouse.
 Non volatile:-Once data is in the data
warehouse,it will not change.So,historical
data in a data warehouse should never be
altered.
 It must be optimized for access to very
large amount of data.
 It is based on client server architecture.
 It is capable of handling dynamic matrices.
 It maintains transparency.
 It is consitent and flexible.

18. DATA WAREHOUSE
USAGE:-
• Three kinds of data warehouse
applications
1)Information processing:-Supports querying, basic
statistical analysis, and reporting using crosstabs, tables,
charts and graphs.
2)Analytical processing:-
• Multidimensional analysis of data warehouse data
• Supports basic OLAP operations, slice-dice, drilling,
pivoting
3)Data mining:-
• Knowledge discovery from hidden patterns
• Supports associations, constructing analytical models,
performing classification and prediction, and presenting
the mining results using visualization tools.
 Differences among the three tasks

19. TRENDS IN DATA
WAREHOUSING
 In the next few years, data warehousing is
expected make big strides in software,
especially for optimizing queries:-
o indexing very large tables
o enhancing SQL
o improving data compression
o expanding dimensional modeling

20.  Real-Time Data Warehousing
 Multiple Data Types
 Adding Unstructured Data
 Searching Unstructured Data
 Spatial Data
 Data Visualization
 Major Visualization Trends
 Visualization Types
 Advanced Visualization TechniquesChart
Manipulation.
 Drill Down.
 Advanced Interaction

21. Architecture of data warehouse
fig:- A three tier data warehousing
Top tier:
Middle tier
Bottom tier
Data
warehouse
server
Backend tools

22. 1)Bottom tier:-The bottom tier is a warehouse
database server that is always a relational
database system.
 Back-end tools and utilities are used to feed data
into the bottom tier from operational databases or
other external sources. These tools and utilities
perform data extraction,cleaning and
transformation as well as load and refresh
functions to update the data warehouse.
 The date extracted using application program
interfaces known as gateways.
 Example of gateways are ODBC(open database
connection)and OLEDB(Open Linking and
embedding for database) by microsoft and
jdbc(java database connecton).
 This tier also contains a metadata repository, which
stores information about the data warehouse and
its contents.

23. 2.)Middle tier:- The middle tier is an OLAP
server that is typically implemented using
either:-
a) A relational OLAP (ROLAP) model that
is,an extended relation DBMS that maps
operations.Intermediate server b/w
relational back-end server and client front
end tools.
b) A multidimentional OLAP (MOLAP) model
that is, a special purpose server that
directly implements multidimentional data
and operations. Supports multidimention
views.

24. 3.)Top tier:-The top tier is a front –end client
layer ,which contains query and reporting
tools ,analysis tools,and or data mining
tools.
 Note:-
OLAP – Online Analytical Processing:
 This is the major task of Data Warehousing
System.
 Useful for complex data analysis and
decision making.
 Market oriented –used by
managers,executives and data analyst.

25. DBMS VS Data
Warehousing
In today’s corporate world ,every
business enterprise ,no matter how
big or small requires a database. The
more the business grows, the more
urgent is the requirement of a
database .The database is required to
keep a check on the growth of a
business in a specific period.

26. DBMS:-DBMS is at times known as the
database manager although it is the
abbreviated form of database
management system.
 It is basically a repertoire of computer
programs that devoted for the
management of the database of an
organization .
 It is a complete and comprehensive
methodology in use for specific purposes
 Like overall management of digital data-
bases,creation and maintenance of
data,searching and serving other
operations relating to the database.

27. DATA WAREHOUSE:- A data warehouse
is usually a place where various types’
data -bases are stored mainly for
purpose of security ,archival analysis
and storage.
 The data warehouse consists of either
one or several computer systems that
are networked together form a single
computer system.
 The data warehouse is a database of a
different kind: an OLAP (online analytical
processing) database. A data warehouse
exists as a layer on top of another database
or databases (usually OLTP databases).

28.  In DBMS,there is OLTP(online
transaction processing )is used.Here we
cannot analysis because data changes
day by day.
 In data warehousing there is
OLAP(online analytical processing .It
maintain historical data.It collects data
from different databases like oracle and
so on.It is used to find analysis and
generate reports.
 The key difference between DBMS and
data warehouse is the fact that a data
warehouse can be treated as a type of
database or a kind of database which
provides special facilities for analysis
and reporting while DBMS is the overall
system which manages a certain

29. for your attention!