The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.

1. Data Warehousing
Kavisha Uniyal
And
Gunjan Bhandari

2.  DEFINITION
• ”A DATA WAREHOUSE is a
subject-oriented, integrated,
time varient, non volatile
collection of data in support
of management decision”

3.  Data Warehouse Features
• Subject-oriented: WH is organized around the major subjects of the
enterprise rather than the major application areas.. This is reflected in the need
to store decision-support data rather than application-oriented data
• Integrated: because the source data come together from different enterprise-
wide applications systems. The source data is often inconsistent using..The
integrated data source must be made consistent to present a unified view of the
data to the users
• Time-variant : the source data in the WH is only accurate and valid at some
point in time or over some time interval. The time-variance of the data
warehouse is also shown in the extended time that the data is held, the implicit
or explicit association of time with all data, and the fact that the data
represents a series of snapshots
• Non-volatile : data is not update in real time but is refresh from OS on a
regular basis. New data is always added as a supplement to DB, rather than
replacement. The DB continually absorbs this new data, incrementally
integrating it with previous data

4. Why Data Warehousing??
“Necessity is the
mother of
invention”

5.  Scenario 1
Cola Pvt Ltd is a company with branches at
Mumbai, Delhi, Chennai and Banglore. The
Sales Manager wants annual sales report to
plan the future production quantity in each
branch. Each branch has a separate
operational system.

6. Scenario 1 : Cola Pvt Ltd.
Mumbai
Delhi
Sales per item type per branch Sales
for a year. Manager
Chennai
Banglore

7. Solution 1:Cola Pvt Ltd.
Mumbai
Report
Delhi
Query & Sales
Data
Analysis tools Manager
Warehouse
Chennai
Banglore

8. Need Of Data Warehouse
• Business Users : require data warehouse to view summarized
data from past. The data is presented in a very simple form such
that it is very easy to understand the facts and figures.
• Store Historic Data : data warehouse is required to store the
time variable data from past.
• Selective Data : when data is stored in DWH it may not be full
data because DWH contains summarized data.
• Differentiate analytical and operational processing:
operational DB is entitled for online transactions and various
operations. And analytical DB is used for analysis the
summarized data.
• Make Strategic Decisions : some strategies may be depending
upon the data in the DWH

9.  3 Tier Architecture

10. 3 Tire Architecture
1. Extraction and Transformation Tier : Data is collected from
various sources and then refined, non useful data is eliminated,
transformed into standard format and then loaded into data
warehouse.
2. Connective Tier : The data mart server serves as connective
or middle tier. Extracted and transformed data from DB is
stored in DWH(central storage)
3. Data Access and Retrieval Tier : End user enters a query
through OLAP tool. The query is processed by WH and the
graphical and complex results are displayed.

11. DATA MART
• Definition of Data Mart
A subset of data warehouse that stores only relevant data
Data Mart is of two types : dependent and independent
• Dependent data mart
A subset that is created directly from a data warehouse
• Independent data mart
A small data warehouse designed for a strategic business
unit or a department

12. Scenario 2
• There are three companies X, Y and Z arranged
along the x-axis. There are three countries India,
China, Japan arranged along y-axis. The two years
2010 and 2011 are shown along the z-axis. The
intersection of each element from x, y and z-axis
gives the sales in lacs of a particular company in a
particular country in a particular year. ‘All’ given
along axis displays the sum of sale in all entities
with the intersecting dimension.

13. All
year 2010
2011
All 31 46 24 101
JAPAN 4 9 3 16
CHINA 7 12 5 24
INDIA 20 25 16 61
country
X Y Z All
company

14. Online Analysis
Processing(OLAP)
• It enables analysts, managers and executives to gain insight into data
through fast, consistent, interactive access to a wide variety of possible
views of information that has been transformed from raw data to
reflect the real dimensionality of the enterprise as understood by the
user.
Data
Warehouse

15. OLAP Operations
1. ROLL UP: The roll uses concept hierarchy which maps
lower level details to higher generalized details.
For eg:
STREET
AREA
CITY
STATE

16. 2.DRILL DOWN: It is opposite of Roll Up. It
goes from higher level details to lower level
details. For eg:
CONTINENT
COUNTRY
STATE

17. 3. SLICE AND DICE: This is used for searching and
accessing data in the cube.
YEAR
COUNTR
Y
COUNTR
Y COMPANY
COMPANY

18. 4. PIVOT OR ROTATE: This operation is
used when a user wants to change the
orientation of the view of cube. In this
operation position of some rows or some
columns may be changed.
year
year
company
country
country
company

19. Difference Between Data
Warehouse and Database
Database Data Warehouse
Orientation Application oriented Subject oriented
Amount of Detail Detailed data Summarized data
Time Dependence Give data at the moment Give data over time
of access
Community served Clerical community Managerial community
volatility Volatile Non-volatile
Availability Highly available Relaxed availability
Redundancy Non-redundant Some redundancies

20. REFERENCE
BOOK:
• Data Mining and Warehousing by KANIKA LAKHANI AND
GAURAV GIRDHAR.
SEARCH ENGINE:
• Google