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DEEPANSHI DUBE GAURAV KALRA
SAKSHI BAJ PREKSHA JAINYASH PAREEK JENTI TAHILIANI
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UNDERSTANDING DATA WAREHOUSING CUSTOMIZING DATA WAREHOUSES DATA MODELING
DATA WAREHOUSING USING OLAP SYSTEM TYPES OF OLAP OLAP V/S OLTP DEVELOPING BUSINESS STRATEGIES
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A Data Warehouse is a
repository database thatefficiently extracts datafrom existing systems for
the rapid analysis andreporting of information in abusiness enterprise.
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RELATIONAL DATABASES ( RDBMS ):-
This organizes data into related rows
and columns as specified by the relationalmodel.
FLAT FILE STRUCTURE :-
This comprises fields that are the samefixed length with records each separatedby a delimiter.
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Formulate Business Strategies
Used for Analysis / EffectiveReport Generation
Collate and consolidate data
Generate complex Query / Report
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DATA VALIDATION
DATA PREPARATION
DATA CLEANSING AND
TRANSFORMATION
DATA LOADING
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A Data Mart is a customized smaller
version of a data warehouse. DataMarts are smaller and focused onthe customization needs of aparticular department and the endusers. These provide inputs to thedecision support system for thedepartment.
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Efficient management of data
The decision support systems can easilyanalyze this information
The cost of data mart is less than thatof data warehouse
The performance of the systems is good.
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Data Modeling is defined as theprocess whereby integrated dataobtained from divergent sources ismodeled to map to the usersperspective.Data modeling is used to
design the data warehouse.
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A Schema is a diagrammatic representationof the relation that exist among a group oftables in a database.
It is used for dimensional modeling
It provides a multi dimensional view to the enduser
Epicenter of this schema is a fact table
Queries that rely on multiple dimension can use theschema to return accurate result
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In a survey, we asked respondentsabout the extent to which they hadachieved eight separate potential
business benefits. The possible levelsvaried from "Proven and Quantified" to"Got Worse/More Expensive." We usedthese responses to calculate weightedscores, plotted in a Figure which can beseen in the next slide
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On-Line Analytical Processing (OLAP) tools meet theneed for interactive multidimensional reporting and
analysis. They allow
operational managers to perform trend, comparative,and time-based
analysis by enabling exploration of pre-calculated andsummarized data
along multiple dimensions. Operational managers can
explore data firstat a summary level, then drill down through the data
hierarchy
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In DATA WAREHOUSE using OLAP system,information exploration and historical trend
analysis requirements of the organizationare assessed.
The data that is analyzed by OLAP tools is
obtained from the DATA WAREHOUSE todetermine the future ratio of success forthe companies.
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Steps in the OLAP Creation Process
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A Data cube stores dimensions,hierarchies and facts, which are
analyzed according to the userperspective.
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Offers real time access to data.Provides a multi-dimensional view.Provides quick access to all
aggregates statements.Management of the DataWarehouse and Business
Intelligence.
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OLAP systems have been traditionallycategorized using the followingtaxonomy
Multidimensional OLAP (MOLAP)
Relational OLAP (ROLAP)
Hybrid OLAP (HOLAP)
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MOLAP is the 'classic' form of OLAP andis sometimes referred to as just OLAP.MOLAP uses database structures that are
generally optimal for attributes.
ROLAP works directly with relational databases.The base data and the dimension tables are storedas relational tables and new tables are created tohold the aggregated information.
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A HOLAP database will use relational tablesto hold the larger quantities of detailed
data, and use specialized storage for atleast some aspects of the smaller quantitiesof more-aggregate or less-detailed data.
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OLTP OLAP
users Clerk,it professional Knowledgeworker
functions Day to dayoperations
Decision support
DB design Application oriented Subject oriented
Usage repetitive Ad-hoc
Unit of
work
Short,simple
transaction
Complex query
DB size 100 MB-GB 100 MB-TB
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The various tools used in developing businessstrategies include :-
Data MiningKnowledge Management System
Business Intelligence Systems
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Data Mining is an activity that extracts
information to discover hidden factsthat are contained in databases.
It is a technique that helps in analyzing
data in an accurate and effectivemanner.
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Knowledge Management System is a process ofadopting knowledge as a strategic asset to drive a
sustainable business advantage. The many benefitsof Knowledge Management System include :-
Improved decision making
Improved customer serviceEnhanced employee skills
Increased profits
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Business Intelligence entails the process of :-
Collecting the right information
Analyzing the collected informationConverting the collected information or data intoa useful form
Distributing the useful form of the data acrossthe enterprise