In my last Blog we discussed about Dimensional Modelling and some of its components. Today we will go through different Schema that can be used during Dimensional Modelling to create a Data Warehouse.
Before we start with today's topic , For my viewers those who are new to this field i would like to revisit some of the key points of my previous blogs:
1) Business Intelligence is mainly divided into three parts as per my understanding a) Data Warehouse design and Implementation (ETL process) b) Data Analysis (Using OLAP cubes) c) Reporting and Dashboard Creation
For further details revisit my First blog
2) Important Components involved in Dimensional Modelling or Data Warehouse Designing a) Fact Tables (Additive Facts, Semi-Additive Facts, Non- Additive Facts) b) Dimension Table c) Grain
For further details revisit my Second blog
After a thorough revision of previous concepts lets start our today's discussion about different Schema involved in Dimensional Modelling or Data Warehouse Designing.
First of all i would like to explain the meaning of the topic i.e
Snow Covered Wagon Hitched to a Star = SnowFlake Schema and Star Schema are two types of Schema that are used while designing a Data Warehouse, Hence they can be explained as follows:
Star Schema: A Star Schema is one of the simplest and easiest schema to understand. A schema which consists of Dimension tables only attached to Fact tables. A Star Schema get its name from its physical representation where we have Fact table at the center with Dimension table surrounding it representing the star points. Star Schema can also be called as a denormalized schema as it contains Dimensional table in the lowest normal form.
