S8-DW OLAP BI

8/2/2019 S8-DW OLAP BI

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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

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