Fox MIS Spring 2011 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 –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

Multidimensional Analysis: OLAP (Online Analytical Processing)

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)

Example: Sales

Multidimensional View of Sales Multidimensional analysis involves viewing data simultaneously categorized along potentially many dimensions

OLAP Server with Multidimensional Data Store Arrangement

Simple OLAP

Slice and Dice

Pivoting

OLAB Cube Example

OLAP Screen Example

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

Star Schema for Sales

Data Warehouse Implementation Road Map