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Fox MIS Spring 2011 Data Warehouse Week 8 Introduction of Data Warehouse Multidimensional Analysis: OLAP

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Data Warehouse. Week 8 Introduction of Data Warehouse Multidimensional Analysis: OLAP. Data Warehouse. Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making. Characteristics of Data Warehouse. Integrated Centralized - PowerPoint PPT Presentation

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Page 1: Data Warehouse

Fox MISSpring 2011

Data Warehouse

Week 8Introduction of Data Warehouse

Multidimensional Analysis: OLAP

Page 2: Data Warehouse

Data Warehouse

• Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making

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Characteristics of Data Warehouse• Integrated

– Centralized– Holds data retrieved from entire organization

• Time Variant – Flow of data through time– Projected data

• Non-Volatile – Data never removed– Always growing

• Subject-Oriented – Optimized to give answers to diverse questions– Used by all functional areas

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Multidimensional Analysis:

OLAP (Online Analytical Processing)

Page 5: Data Warehouse

• Advanced data analysis environment• Supports decision making, business modeling,

and operations research activities

• Characteristics of OLAP– Use multidimensional data analysis

techniques– Provide advanced database support– Provide easy-to-use end-user interfaces– Support client/server architecture

Online Analytical Processing (OLAP)

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Example: Sales

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Multidimensional View of Sales• Multidimensional analysis involves viewing data

simultaneously categorized along potentially many dimensions

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OLAP Server with Multidimensional Data Store Arrangement

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Simple OLAP

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Slice and Dice

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Pivoting

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OLAB Cube Example

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OLAP Screen Example

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OLAP Screen Example

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Data Warehouse Modeling: Star Schema

• Data-modeling technique • Also called star-join schema, data cube, or multi-dimensional

schema• The simplest style of data warehouse schema. • The star schema consists of one or more fact tables referencing any

number of dimension tables• Maps multidimensional decision support into relational database• Yield model for multidimensional data analysis while preserving

relational structure of operational DB

• Facts– The fact table holds the main data. It includes a large amount of

aggregated data, such as price and units sold• Dimensions

– Dimension tables, which are usually smaller than fact tables, include the attributes that describe the facts.

• Attributes

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Star Schema for Sales

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Data Warehouse Implementation Road Map