Data Modelling in Data warehousing Part 2

In Data warehouse,We process large amount of data and very few updates or deletes.
The data warehouse has mixed modeling approach of relational modeling and dimensional modeling.

Relational Modeling or Entity-Relationship Modeling is used to illuminate the microscopic relationships among data elements.It removes data redundancy.It makes transaction processing very simple and deterministic.It is more suited for Traditional modeling technique, OLTP and corporate data warehouse.But for the enterprise needs,it become more complex.End users cannot understand or remember an ER model.So the dimensional model comes into picture.

From data warehouse,it is moved to Data mart which is specific to a business process. The data here is kept in denormalized form. It is mostly in dimensional model or flat model..From data mart the reports are created or the data can be pulled by OLAP system for reporting.
Dimensional modeling approach is

• best way to model decision support data.
• only technique for delivering data to end users in a data warehouse.
• a logical design technique that seeks to present the data in a standard framework.

Every dimensional model is composed of one table with a multi-part key called fact table,and a set of smaller tables called dimension tables,which i describe in my earlier posts.Each dimension table has a single part primary keythat corresponds exactly to one of the components of the multipart key in the fact table.This characteristic star-like structure is called a star-join.Examples: sales by city, sales by state, sales by country, sales by region etc are facts.The data from multiple tables of city, state, country, region of source system are stored in Location Dimension.The sales table contain foriegn keys which are the primary keys of dimension tables which together forms a star schema.