Unilever is a multinational company with branches in several countries that wants to analyze quarterly sales reports. Currently, each branch stores data separately in different systems. A data warehouse is proposed to integrate the sales data from each branch into a central repository to generate reports. The president of a similar company, Hindustan Unilever, also wants sales information to make decisions and expand the business. An example data warehouse model is presented with dimensions for product, time, region/country and measures for units sold and revenue.

1. Introduction to Data Warehousing
By
MAHESH.AMPOLU

2. Necessity is the mother of invention
Why Data Warehouse?

3. Scenario
Unilever is a company with branches at UK,
India, America and Japan. The Sales Manager
wants quarterly sales report. Each branch has a
separate operational system.

4. Scenario 1 : Unilever company.
India
UK
Sales per item type per branch Sales
for first quarter. Manager
America
Japan

5. Solution : Unilever company.
 Extract sales information from each database.
 Store the information in a common repository at a
single site.

6. Solution : Unilever company.
India
Report
UK
Query & Sales
Data Analysis tools Manager
Warehouse
America
Japan

7. Scenario :
Hindustan Unilever is a small,new company.
President of the company wants his company should
grow. He needs information so that he can make
correct decisions.

8. Solution :
 Improve the quality of data before loading it
into the warehouse.
 Perform data cleaning and transformation
before loading the data.
 Use query analysis tools to support adhoc
queries.

9. Solution
Expansio
n
sales
Data Query and Analysis President
Warehouse tool
time
Improvemen
t

10. What is Data Warehouse??

11. Inmons’s definition :
A data warehouse is
-subject-oriented,
-integrated,
-time-variant,
-nonvolatile
collection of data in support of management’s
decision making process.

12. Subject-oriented
 Data warehouse is organized around subjects such
as sales,product,customer.
 It focuses on modeling and analysis of data for
decision makers.
 Excludes data not useful in decision support
process.

13. Integration
 Data Warehouse is constructed by integrating
multiple heterogeneous sources.
 Data Preprocessing are applied to ensure
consistency.
RDBMS
Data
Legacy Warehouse
System
Flat File Data Processing
Data Transformation

14. Integration
 In terms of data.
– encoding structures.
– Measurement of
attributes.
– physical attribute.
of data remarks
– naming conventions.
– Data type format

15. Time-variant
 Provides information from historical perspective
e.g. past 5-10 years
 Every key structure contains either implicitly or
explicitly an element of time

16. Nonvolatile
 Data once recorded cannot be updated.
 Data warehouse requires two operations in data
accessing
– Initial loading of data
– Access of data
load
access

17. Data Warehousing Architecture

18. Data Warehouse Architecture
 Data Warehouse server
– almost always a relational DBMS,rarely flat
files
 OLAP servers
– to support and operate on multi-dimensional
data structures
 Clients
– Query and reporting tools
– Analysis tools
– Data mining tools

19. OLTP vs OLAP

20. Data Warehouse Schema
 Star Schema
 Fact Constellation Schema
 Snowflake Schema

21. Star Schema
A star schema consists of at least one
fact table and a number of dimension
tables.
 Star
Schema is highly recommended
schema for SSAS cubes.

22. Star Schema
Store Dimension Fact Table Time Dimension
Store Key Store Key Period Key
Store Name Product Key Year
City Period Key Quarter
State Units Month
Region Price
Product Key
Product Desc
Product
Dimension
Benefits: Easy to understand, easy to define hierarchies, reduces
no. of physical
joins.

24. SnowFlake Schema
 Variant of star schema model.
 A single,large and central fact table and one
or more tables for each dimension.
 Dimension tables are normalized i.e. split
dimension table data into additional tables

25. SnowFlake Schema
Store Dimension Fact Table Time Dimension
Store Key Period Key
Store Key
Product Key Year
Store Name
Period Key Quarter
City Key
Units Month
Price
City Dimension
City Key
Product Key
City
Product Desc
State
Region Product
Dimension
Drawbacks: Time consuming joins,report generation slow

26. Fact Constellation
 Multiple fact tables share dimension tables.
 This schema is viewed as collection of stars
hence called galaxy schema or fact
constellation.
 Sophisticated application requires such
schema.

27. Fact Constellation
Sales Shipping
Fact Table Product
Dimension Fact Table
Store Key
Shipper Key
Product Key Product Key Store Key
Period Key Product Desc Product Key
Units
Period Key
Price
Units
Price
Store Dimension
Store Key
Store Name
City
State
Region

28. Fact Constellation
Sales Shipping
Fact Table Product
Dimension Fact Table
Store Key
Shipper Key
Product Key Product Key Store Key
Period Key Product Desc Product Key
Units
Period Key
Price
Units
Price
Store Dimension
Store Key
Store Name
City
State
Region

29. Building Data Warehouse
 Data Selection
 Data Preprocessing
– Fill missing values
– Remove inconsistency
 Data Transformation & Integration
 Data Loading
Data in warehouse is stored in form of fact tables
and dimension tables.

30. Case Study
 Unilever is a new company which produces
soaps,paste and baverages products with
production unit located at NA.
 There products are sold in North,North West and
Western region of India.
 They have sales units at India, America , UK and
Japan.
 The President of the company wants sales
information.

31. Sales Information
Report: The number of units sold.
113
Report: The number of units sold over time and date
January February March April
14 41 33 25

32. Sales Information
Report : The number of items sold for each product with
time
Jan Feb Mar Apr
Soaps 6 17
Paste 6 16 6 8
Time
bread 8 25 21
Product

33. Sales Information
Report: The number of items sold in each country for each
product with time
Jan Feb Mar Apr
India Soaps 3 10 City
Paste 3 16 6
bread 4 16 6
Time
UK soaps 3 7
paste 3 8 Product
bread 4 9 15

34. Sales Information
Report: The number of items sold and income in each region for
each product with time.
Jan Feb Mar Apr
Rs U Rs U Rs U Rs U
India Soaps 7.44 3 24.80 10
Paste 7.95 3 42.40 16 15.90 6
bread 7.32 4 29.98 16 10.98 6
UK Soaps 7.44 3 17.36 7
paste 7.95 3 21.20 8
bread 7.32 4 16.47 9 27.45 15

35. Sales Measures & Dimensions
 Measure – Units sold, Amount.
 Dimensions – Product,Time,Region.

36. Sales Data Warehouse Model
Fact Table
Country Product Month Units Rupees
India Soaps January 3 7.95
India Paste January 4 7.32
UK Soaps January 3 7.95
UK Paste January 4 7.32
India Bread February 16 42.40

37. Sales Data Warehouse Model
City_ID Prod_ID Month Units Rupees
1 589 1/1/1998 3 7.95
1 1218 1/1/1998 4 7.32
2 589 1/1/1998 3 7.95
2 1218 1/1/1998 4 7.32
1 589 2/1/1998 16 42.40

38. Sales Data Warehouse Model
Product Dimension Tables
Prod_ID Product_Name Product_Category_ID
589 Soaps 1
590 Paste 1
288 Bread 2
Product_Category_Id Product_Category
1 domestic
2 food

39. Sales Data Warehouse Model
Region Dimension Table
City_ID City Region Country
1 India West India
2 UK NorthWest India

40. Sales Data Warehouse Model
Time
Product
Sales Fact Product
Category
Region

41. Data Warehousing includes
 Build Data Warehouse
 Online analysis processing(OLAP).
 Presentation.
Cleaning ,Selection &
Integration
RDBMS Presentation
Flat File
Client
Warehouse & OLAP server

42. Thank You