A database is a structured collection of records that can be easily retrieved. The relational model, developed by Edgar Codd in 1969, is one of the most popular database models. Organizations store employee and customer information in databases, and websites store user login information in databases. Data warehousing involves integrating data from different sources into a single database to facilitate reporting and analysis. It allows organizations to consolidate data to better understand business metrics and make strategic decisions.

1. By - Shaik Yasir Ahmed

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. 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 data poorly documented
• I can’t use the data I found
• results are unexpected
• data needs to be transformed from one
form to other
6

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
7

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

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

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

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

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

15. Prod_Dim Org_Dim
Prod_Id Org_Id
……… SalesOrder_Fact ………
Cust_Id
Prod_Id
Order_Date
Delivery_Date
Org_Id
Unit_Price Time_Dim
Cust_Dim Qty Date
Cust_Id Total_Amount Year
……… Tax Month
………
STAR Schema

16. Product_Dim SalesOrder_Fact
Prod_Id Cust_Id
Prod_Name Prod_Id
Base_Rate Order_Date
Cat_Name Delivery_Date
Cat_Desc Unit_Price
Group_Name Qty
Group_Desc Total_Amount
Tax

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

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]
18

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
19

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

23. Oracle 10g
IBM DB2
Base Product
$ 25K $ 40K $ 25K

24. Tuning
$3K
Diagnostics
$3K
Partitioning Performance
$10K Expert
(included)
$10K
Manageability
Base Product
$ 25K $ 40K
56K $ 25K
35K

25. DB2 OLAP
$35K
DB2
Warehouse
OLAP $75K
$20k Cube Views
Mining $9.5K
$20k
BI Bundle
$20k
Business
Intelligence
(included)
Manageability
Base Product
$ 25K $ 116K
$ 56K $ $ 35K
154.5K

26. Data Guard
$116K Recovery
Expert
$10k
High Availability
Business
Intelligence
(included)
Manageability
Base Product
$ 25K $ 116K
232K $ 154.5K
164.5K

27. $116K - $164.5K
$232K
Multi-core
High Availability
Business
Intelligence
(included)
Manageability
Base Product
$ 25K $$464k-
232K
$348k $$164.5K
329K

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

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

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

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