By - Shaik Yasir Ahmed

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.

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.

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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

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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

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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 ??

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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 product prom--otions have the biggest impact on revenue?

What product prom--otions have the biggest impact on revenue?

What is the most effective distribution channel?

What is the most effective distribution channel?

Data warehouse helps any Business in Many WaysLet’s say A producer wants to know….

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.”

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.

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.

Product

Prod_Id

Prod_Name

Base_Rate

Cat_IdCategory

Cat_Id

Cat_Name

Cat_Desc

Group_IdGroup

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

2. Slowly changing Dimensions1. Types of Dimensions

3. Hierarchies

Normalized Tables De-Normalized Tables

SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Unit_Price

Qty

Total_Amount

Tax

SalesOrderDetails

Cust_Id

SalesPerson

Prod_Id

Order_Date

Booked_Date

Delivery_Date

Unit_Price

Qty

Tax

Created_By Qty*Unit_Price+Tax=Total AmountUsually calculate all the calculations before storing into OLAP

Reference keys of Dimensions

Numeric fields called as Fact or measure

Prod_Dim

Prod_Id

………

Cust_Dim

Cust_Id

………

Time_Dim

Date

Year

Month

………

Org_Dim

Org_Id

………SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Org_Id

Unit_Price

Qty

Total_Amount

Tax

STAR Schema

Product_Dim

Prod_Id

Prod_Name

Base_Rate

Cat_Name

Cat_Desc

Group_Name

Group_Desc

SalesOrder_Fact

Cust_Id

Prod_Id

Order_Date

Delivery_Date

Unit_Price

Qty

Total_Amount

Tax

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.

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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]

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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

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Also Data Mining works with Warehouse Data

Data Warehousing provides the Enterprise with a memory

Data Mining provides the Enterprise with intelligence

Base ProductBase Product$ 25K $ 40K $ 25K

Oracle 10g

IBM DB2

Base ProductBase Product

ManageabilityManageability

(included)(included)

$ 25K $ 40K $ 25K $ 56K $ 35K

Tuning $3K

Diagnostics $3K

Partitioning $10K

Performance

Expert$10K

Base ProductBase Product

ManageabilityManageability

(included)(included)

$ 25K $ 35K $ 154.5K $ 56K$ 116K

Business Business IntelligenceIntelligence

OLAP $20k

Mining$20k

BI Bundle$20k

DB2 OLAP $35KDB2

Warehouse $75K

Cube Views $9.5K

Base ProductBase Product

ManageabilityManageability

(included)(included)

$ 25K $ 154.5K $ 164.5K $ 232K$ 116K

Business Business IntelligenceIntelligence

High AvailabilityHigh Availability

Data Guard $116K Recovery

Expert$10k

Base ProductBase Product

ManageabilityManageability

(included)(included)

High AvailabilityHigh Availability

Business Business IntelligenceIntelligence

Multi-coreMulti-core

$348k - $464k

$ 232K$ 25K $ 164.5K $ 329K

$164.5K$116K - $232K

OperationalData Sources

Data-Migration Middleware (Populations-Tools)

DataStorage

RepositoryRepository

DataAnalysis

Reporting, OLAP,Data Mining

Additional BenefitAdditional BenefitNumber of UsersNumber of Users

What happened?

What happened?

Why did it happen?Why did

it happen?

What will happen?

What will happen?

What happened why and how?

What happened why and how?

Stage DB Optional

ROLAP

OLTP

MOLAP

O L A P

SSIS

Integration Services Analysis Services

Reporting Services

SSAS

SSRS

SSISData Marts

CUBE

OLTP – Online Transaction ProcessingOLAP – Online Analytical ProcessingMOLAP – Multidimensional OLAPROLAP – Relational OLAPHOLAP – Hybrid OALP Dimensions – De-normalized master tables Attributes – Columns of DimensionsHierarchies – sequential order of attributesFacts (Measure group) – Transactions tables in DWHFact (Measures)Cubes – Multidimensional storage of DataKPI’s – Key performance indicatorDashboards – combination of reports,kpis,chartsData Marts – Subjective Collection of DataSCD’s – Slowly changing DimensionsPerspectives – Child Cube