Dimensional Data Modeling :

It is a modeling technique used in data warehousing systems. It is different from ER modeling technique used in OLTP systems. In Dimensional modeling a model of tables is combined together with aim of optimized query performance in Decision Support systems in relational databases.

Before further going into details of dimensional data modeling we should be aware of key concepts in Dimensional Data Modeling

Dimension : It can be considered as category of information. For example geographic dimension. It provides a way to slice and dice the data in data warehouse.

Attribute : Attribute can be considered as characteristic of dimension. For example region is attribute of geographic dimension. Dimension attribute is column in dimension table.

Hierarchy : It represents relationships between attributes of dimension. It defines different level with in dimension, In case of geographic dimension it is like

Continent ->Country –> Region –> State ->City ->Street

Fact Table : It represents the measure of interest, for organization product revenue is measure of interest. The level of granularity is an important factor in designing fact tables. For example it can be revenue by continent or revenue by country. In this example fact table will have three columns Product, Geographical region, Revenue.

Lookup Table: The look up table represents a dimension and constitutes attributes for dimension. Lookup table for product will consist of all products available.

Dimensional Modelling Vs ER Modelling :
In Dimensional Modelling emphasis is on optimising decision support query performance,On other hand ER Model are focussed on

1. Removing redundancy in data model
2. Optimse OLTP Performance
3. Focus is on retrieval of single record

We will continue with dimensional data modelling in next post.