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7/31/2019 MSBI basics
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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 managements decision-making process.
ERPwill Run the Business
- like how Tyres Run the CarBI (Reports,Data mining,Dashboards,kpis)will help you to take business decisions basedon your historical data.
- like Steering, mirrors, breaks,
dashboards will help, how smoothly you canrun the Car or reach the Destination.
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Which are ourlowest/highest margincustomers ?
Who are my customersand what products
are they buying?
Which customers
are most likely to goto the competition ?
What impact willnew products/serviceshave on revenue
and margins?
What product prom-
-otions have the biggestimpact on revenue?
What is the mosteffective distributionchannel?
In What way a Data warehouse helps any Business
Lets say A producer wants to know.
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Data, Data everywhere yet ... I cant find the data I need
data is scattered over the network many versions, subtle differences
I cant get the data I need
need an expert to get the data
I cant understand the data I found
available data poorly documented
I cant 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 availableto 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 makingdecisions that were not previous
possible
A decision support database
maintained separately from the
organizations 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
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We want to know ...
Given a database of 100,000 names, which persons are the least likely todefault on their credit cards?
Which types of transactions are likely to be fraudulent given thedemographics 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 than5,000, how many lost responses will result?
If I emphasize ease-of-use of the product as opposed to its technicalcapabilities, 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
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Base Product
$ 25K $ 40K $ 25K
Oracle 10gIBM DB2
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Base Product
Manageability
(included)
$ 25K $ 40K $ 25K$ 56K $ 35K
Tuning$3K
Diagnostics$3K
Partitioning$10K
PerformanceExpert$10K
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Base Product
Manageability
(included)
$ 25K $ 35K$ 154.5K$ 56K$ 116K
BusinessIntelligence
OLAP$20k
Mining$20k
BI Bundle$20k
DB2 OLAP$35K
DB2Warehouse$75K
Cube Views$9.5K
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Base Product
Manageability
(included)
$ 25K $ 154.5K$ 164.5K$ 232K$ 116K
BusinessIntelligence
High Availability
Data Guard$116K Recovery
Expert$10k
$116K
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Base Product
Manageability
(included)
High Availability
BusinessIntelligence
Multi-core
$348k -$464k$ 232K$ 25K $ 164.5K$ 329K
$164.5K$116K -$232K
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Additional BenefitNumber of Users
What
happened?
Why did
it happen?
What will
happen?
What happened
why and how?
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OLTP Online Transaction ProcessingOLAP Online Analytical ProcessingMOLAP Multidimensional OLAPROLAP Relational OLAPHOLAP Hybrid OALP
Dimensions De-normalized master tablesAttributes Columns of DimensionsHierarchies sequential order of attributesFacts (Measure group) Transactions tables in DWHFact (Measures)Cubes Multidimensional storage of Data
KPIs Key performance indicatorDashboards combination of reports,kpis,chartsData Marts Subjective Collection of DataSCDs Slowly changing DimensionsPerspectives Child Cube
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Operational
Data Sources
Data-Migration
Middleware (Populations-Tools)
DataStorage
Repository
Data
AnalysisReporting, OLAP,
Data Mining
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Stage DB
Optional
ROLAP
OLTP
MOLAP
O L A P
Integration Services Analysis
Services
Reporting
Services
SSAS
SSRS
SSISData Marts
CUBE
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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-Normalizedform.
5. For Designing OLTP we used datamodeling.
5. For Designing OLTP we usedDimension modeling.OLAP is classified into two i.e.,MOLAP & ROLAP
4. We Called the Storage objects asTables. i.e., All the masters and theTransactions are stored in the tables.
4. We Called the Storage objects asDimension and Facts. i.e., All the mastersAre dimension and the Transactions are
Facts.
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Product
Prod_Id
Prod_Name
Base_Rate
Cat_IdCategory
Cat_Id
Cat_Name
Cat_Desc
Group_IdGroupGroup_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 Dimensions
1. Types of Dimensions
3. Hierarchies
Normalized Tables De-Normalized Tables
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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 calculationsbefore storing into OLAP
Referencekeys of
Dimensions
Numericfieldscalled as
Fact ormeasure
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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_PriceQty
Total_Amount
Tax
STAR Schema
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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_AmountTax
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1. Dimensions will have onlyrelation with the Fact.
(Normalized model)
1. Dimension will have arelation other than Fact. (De-
Normalized model)
2. One to many or One toOne relation will Occur.
2. Used for many to manyrelation.
3. Performance is fast butrequired huge storage space.
3. Performance is Low butrequired Less storage space.
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