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4. DataBase (DB) –
A place where the collection of records will be maintained in a structured format so that It
can be easily retrieved when ever required is known as a database.
One of the most popularly used database model is the
relational model. It was developed by Edgar Codd in
1969.
Example :
How do you think the Organizations store their
employee and customer information? they store it in
a database.
where do you think the website maintains the login
information about their users?
they store it in a database.

5. ERP–
ERP, which is an abbreviation for Enterprise
Resource Planning, is principally an integration
of business management practices and modern
technology.
ERP is a business tool that management uses to
operate the business day-in and day-out.
OLTP–
OLTP, which is an abbreviation for Online Transaction
processing, handle real time transactions which inherently
have some special requirements. If your running a Bank, for
instance, you need to ensure that as people withdrawing
money from ATM’S they are properly and efficiently updating
the database also those transactions are properly effecting to
their Accounts.

6. 6
Data, Data everywhere yet ...
• I can’t find the data I need
– data is scattered over the network
• I can’t get the data I need
• need an expert to get the data
• I can’t understand the data I
found
• available • I can’t use d tahtae pdoaotraly Id ofocuumnednted
• results are unexpected
• data needs to be transformed from
one form to other

7. 7
What are the users saying...
• Data should be integrated across
the enterprise
• Summary data has a real value to
the organization
• Historical data holds the key to
understanding data over time
• What-if capabilities are required

8. In What way I can Answer the above question with
8
my OLTP system...
Is Data Warehousing is the Solution ?? YES
Can I Improve my
business using Data
warehousing ??
YES.. How ??

9. 9
Data warehouse helps any Business in Many
Which are our
lowest/highest margin
customers ?
Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
Who are my customers
and what products
are they buying?
Which customers
are most likely to go
to the competition ?
Which customers
are most likely to go
to the competition ?
What impact will
new products/services
have on revenue
and margins?
What impact will
new products/services
have on revenue
and margins?
What is the most
effective distribution
channel?
What is the most
effective distribution
channel?
What product prom-
-otions have the biggest
impact on revenue?
What product prom-
-otions have the biggest
impact on revenue?
Ways
Let’s say A producer wants to know….

10. DWH – (Data Warehousing)
It usually contains historical data derived from transaction data, but it can include data
from other sources. It separates analysis workload from transaction workload and
enables an organization to consolidate data from several sources.
Raugh kimball –
In simplest terms Data Warehouse can be
defined as collection of Data marts.
-Data marts : Subjective collection of Data.
Bill Inmon –
A data warehouse is a “subject-oriented,
integrated, time variant and nonvolatile” collection of
data in support of management’s decision-making
process.”

11. OLAP – (Online Analytical Processing)
The ability to analyze metrics in different dimensions such as time, geography, gender,
product, etc. For example, sales for the company is up. What region is most responsible for
this increase? Which store in this region is most responsible for the increase? What
particular product category or categories contributed the most to the increase? Answering
these types of questions in order means that you are performing an OLAP analysis.
OLAP servers provides better performance for
accessing multidimensional data. The most important
mechanism in OLAP which allows it to achieve such
performance is the use of aggregations.
Aggregations are built from the fact table by
changing the granularity on specific dimensions and
aggregating up data along these dimensions.
OLAP systems gives analytical capabilities that are
not in SQL or are more difficult to obtain.

12. 1. OLTP (on-line transaction processing)
2. Day-to-day operations: purchasing,
inventory, banking, manufacturing, payroll,
registration, accounting, etc.
1. OLAP (on-line analytical processing)
2. Data analysis and decision making
3. The tables are in the Normalized form. 3. The tables are in the De-Normalized
form.
5. For Designing OLTP we used data
modeling.
5. For Designing OLAP we used
Dimension modeling.
OLAP is classified into two i.e.,
MOLAP & ROLAP
4. We Called the Storage objects as
Tables. i.e., All the masters and the
Transactions are stored in the tables.
4. We Called the Storage objects as
Dimension and Facts. i.e., All the masters
Are dimension and the Transactions are
Facts.

13. Product
Prod_Id
Prod_Nam
e
Base_Rate
Cat_Id
Category
Cat_Id
Cat_Name
Cat_Desc
Group_Id
Group
Group_Id
Group_Name
Group_Desc
Product_Dim
Prod_Id
Prod_Name
Base_Rate
Cat_Name
Cat_Desc
Group_Name
Group_Desc
Topics Later We will Cover
1. Types of Dimensions
3. Hierarchies
2. Slowly changing Dimensions
Normalized Tables
De-Normalized
Tables

14. SalesOrderDetails
SalesOrder_Fact
Cust_Id
Cust_Id
SalesPerson
Prod_Id
Prod_Id
Order_Date
Order_Date
Delivery_Date
Booked_Date
Unit_Price
Delivery_Date
Qty
Unit_Price
Total_Amount
Qty
Tax
Tax
Created_By Qty*Unit_Price+Tax=Total Amount
Reference
keys of
Dimensions
Numeric
fields
called as
Fact or
measure
Usually calculate all the calculations
before storing into OLAP

15. Prod_Di
m
Prod_Id
………
Cust_Di
m
Cust_Id
………
Org_Dim
Org_Id
SalesOrder_F ………
act
Cust_Id
Prod_Id
Order_Date
Delivery_Date
Org_Id
Unit_Price
Qty
Total_Amount
Tax
Time_Di
m
Date
Year
Month
………
STAR Schema

16. Product_Di
m
Prod_Id
Prod_Name
Base_Rate
Cat_Name
Cat_Desc
Group_Na
me
Group_Des
c
SalesOrder_Fact
Cust_Id
Prod_Id
Order_Date
Delivery_Date
Unit_Price
Qty
Total_Amount
Tax

17. 1. Dimensions will have only
relation with the Fact.
(Normalized model)
1. Dimension will have a
relation other than Fact. (De-
Normalized model)
2. One to many or One to
One relation will Occur.
2. Used for many to many
relation.
3. Performance is fast but
required huge storage space.
3. Performance is Low but
required Less storage space.

18. 18
A single, complete and
consistent store of data
obtained from a variety of
different sources made
available to end users in a
what they can understand
and use in a business
context.
[Barry Devlin]

19. 19
Data Warehousing --
It is a process
• Technique for assembling and
managing data from various
sources for the purpose of
answering business questions.
Thus making decisions that were
not previous possible
• A decision support database
maintained separately from the
organization’s operational
database

20. 20
Also Data Mining works with
Warehouse Data
Data Warehousing provides the
Enterprise with a memory
Data Mining provides the
Enterprise with
intelligence

23. BBaassee PPrroodduucctt
Oracle 10g
IBM DB2
$ 25K $ 40K $ 25K

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BBaassee PPrroodduucctt
((iinncclluuddeedd))
Tuning
$3K
Diagnostics
$3K
Partitioning
$10K Performance
Expert
$10K
$ 25K $$ 4506KK $$ 2355KK

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$ 25K $$ 15166KK $ $1 5345.K5 K
BBuussiinneessss
IInntteelllliiggeennccee
OLAP
$20k
Mining
$20k
BI Bundle
$20k
DB2 OLAP
$35K
DB2
Warehouse
$75K
Cube Views
$9.5K

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MMaannaaggeeaabbiilliittyy
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BBuussiinneessss
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Data Guard
$116K Recovery
Expert
$10k

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$116K - $164.5K
$232K
$348k -
$464k $ 25K $ 232K $$ 1 36249.5KK

28. Data
Storage
Data-Migration Middleware (Populations-Tools)
Operational
Data Sources
Repository
Data
Analysis
Reporting, OLAP,
Data Mining

29. Additional Benefit
What
happened?
Number of Users
Why did
it happen?
What will
happen?
What happened
why and how?

30. Stage DB
Optional
O L A P
ROLAP
OLTP
MOLAP
SSIS
CUBE
Integration Services Analysis
Services
Reporting
Services
SSAS
SSRS
SSIS
Data Marts

31. OLTP – Online Transaction Processing
OLAP – Online Analytical Processing
MOLAP – Multidimensional OLAP
ROLAP – Relational OLAP
HOLAP – Hybrid OALP
Dimensions – De-normalized master tables
Attributes – Columns of Dimensions
Hierarchies – sequential order of attributes
Facts (Measure group) – Transactions tables in DWH
Fact (Measures)
Cubes – Multidimensional storage of Data
KPI’s – Key performance indicator
Dashboards – combination of reports,kpis,charts
Data Marts – Subjective Collection of Data
SCD’s – Slowly changing Dimensions
Perspectives – Child Cube