By - Shaik Yasir Ahmed
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, timevariant,and nonvolatile” collection of data in support of management’s decision-making process.”ERP
will Run the Business- like how Tyres Run the Car
BI (Reports,Data mining,Dashboards,kpi’s)will help you to take business decisions based on your historical data. - like Steering, mirrors, breaks, dashboards will help, how smoothly you can run the Car or reach the Destination.
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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?
In What way a Data warehouse helps any BusinessLet’s say A producer wants to know….
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Data, Data everywhere yet ...• I can’t find the data I need
– data is scattered over the network– many versions, subtle differences
• 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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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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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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A process of transforming data into information and making it available to users in a timely enough manner to make a difference
[Forrester Research, April 1996]
Data
Information
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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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Data Mining works with Warehouse Data
Data Warehousing provides the Enterprise with a memory
Data Mining provides the Enterprise with intelligence
We want to know ...•Given a database of 100,000 names, which persons are the least likely to default on their credit cards?
•Which types of transactions are likely to be fraudulent given the demographics and transactional history of a particular customer?
•If I raise the price of my product by Rs. 2, what is the effect on my ROI?
•If I offer only 2,500 airline miles as an incentive to purchase rather than 5,000, how many lost responses will result?
•If I emphasize ease-of-use of the product as opposed to its technical capabilities, what will be the net effect on my revenues?
•Which of my customers are likely to be the most loyal?
Data Mining helps to extract such information
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
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?
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
OperationalData Sources
Data-Migration Middleware (Populations-Tools)
DataStorage
RepositoryRepository
DataAnalysis
Reporting, OLAP,Data Mining
Stage DB Optional
ROLAP
OLTP
MOLAP
O L A P
SSIS
Integration Services Analysis Services
Reporting Services
SSAS
SSRS
SSISData Marts
CUBE
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 OLTP 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.