s8-dw olap bi
TRANSCRIPT
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Chapter 11 1
Data Warehousing
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Chapter 11 2
Evolution of Data Warehouse
To offer more efficient and cost-effectiveservices to the customer Byautomating business processes
Resulted in accumulation of growingamounts of data in operational databases.
What is database?
Why database?
What is its main objective?
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Chapter 11 3
Database: Characteristics
Automatic Optimized
Updated
Quick Accurate
Atomicity
Consistency Integrity
Durable
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Chapter 11 4
Database for Transaction ProcessSystem: OLTPs
ODS: Operational Data Stores
Database for TPS.
Benefits to operational portions of
business. It provides detail data.
It is optimized for frequent access
It provides faster response times.
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Chapter 11 5
Is there any problem inDatabases/ Operational
Systems?
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Chapter 11 6
Problems in Database/ Operational
Systems
Does Not support Decision-making
History is lost
Time consuming, Duplications
Design Garbage, Disparate systems
Multi-dimensional view not possible
Not for entire organization
Operational systems with overlapping andsometimes contradictory definitions,inconsistent
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Chapter 11 7
Question
How to use operational data (ODS) tosupport decision-making, as a means of
gaining competitive advantage?
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Chapter 11 8
The Data Warehouse
A Data Warehouse is a repository of* Subject-Oriented
* Historical data
* Easily accessible: Access Tools* Ready for Analytical Processing
* Exclusively for Decision-Making activities
* For the Entire Enterprise
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Chapter 11 9
Data Warehouse: What it Stores?
Organized around major subjects (decision-support data) of the enterprise (e.g.customers, products, sales) rather than majorapplication areas (application-oriented data;
of the enterprise (e.g. customer invoicing,stock control, product sales).
The integrated data source must be made
consistent to present a unified view of thedata to the users.
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Chapter 11 10
The Data Warehouse Continued
Characteristics:A Series of Snapshots
Snapshot: Data is only accurate and validat some point in time or over some timeinterval.
Time variant. Stores past data Nonvolatile. Not updated in real-time
Relational. Starflake/ Snowflake Schema
Client/server. Providing end user aneasy access to its data.
Web-based. Support for Web-basedapplications
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Chapter 11 11
The Data Warehouse Continued
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Chapter 11 12
The Data MartA data mart ** small scaled-down version of a DW
** designed for a department or SBU** Contain less information compare to DW** Response time better than DW** Easier accessibility than DW.
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Chapter 11 13
The Data Cube
Example-1: Quantities of a product sold by *specificretail locations during *certain time periods by*salesperson.
Example-2: Sales volume by *department, by *day,by *month, by *year for a *specific region
Cubes provide faster: Queries, Slices and Dices of theinformation, Rollups, Drill Downs
Multidimensional databases (sometimes calledOLAP) ** Data in these databases: Cubes
** Data Cubes: Preprocessed Query** Organize facts by dimensions, such as geographical region,
product line, salesperson, time.
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Chapter 11 14
Business Intelligence
Business intelligence (BI):
A broad category of applications and
techniques
For gathering, storing, analyzing andproviding access to data.
Better business and strategic decisions.
Major applications include the activities of
query and reporting, OLAP, DSS, data
mining, forecasting and statistical analysis.
Starts with Knowledge DiscoveryStarts with Knowledge Discovery
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Chapter 11 15
Business Intelligence Continued
How It Works.How It Works.
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Chapter 11 16
Comparison of OLTP Systems and
Data Warehousing
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Chapter 11 17
Data Warehouse Queries
End-user access tools include: Reporting, query, and application
development tools
Executive information systems (EIS)
OLAP tools Data mining tools
The above tools can be categorised on the
basis of the capability of handling simple to
complex queries.
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Chapter 11 18
Examples of Typical DW Queries
Simple Queries
Complex Queries
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Chapter 11 19
Problems of Data Warehousing
Underestimation of resources for dataloading
Hidden problems with source systems
Required data not captured
Increased end-user demands
Data homogenization
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Chapter 11 20
Problems of Data Warehousing
High demand for resources
Data ownership
High maintenance
Long duration projects
Complexity of integration