Data Modelling vs Dimensional Modelling

Data have become a real resource of interest across most industries in every field which is exactly considered the gateway to competitive advantage and disruptive strategy. Now a day, most of the enterprises are focusing on methods enabling simpler use of data to drive their business and advancement in data related technologies because data is high-volume and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation. 

If we are talking about the data models then it has been treated in two different terminologies such as data modeling and dimension modeling which are differ from each other because Steve Hoberman is stated in his book called Data Modeling Made Simple the distinction between the two types of models is this: 

• Relational Data Models captures the business solution for how part of the business works, a.k.a business process
• Dimensional Data Models capture the details the business needs to answer questions about how well it is doing

It can be claimed that a relational model can also be used as a foundation upon which to answer business questions, but at a strategic level. "How many orders are in an unfulfilled status for the customer XXX due to credit hold?" But the distinction is that of where the reporting question needs the 'native grain' of the table and when the reporting question can be answered with summarized data.

To better understanding, we can take an example of Historical Sale Data having product, state and sales cash value as a relational point-of-view-

Product	State	Sales Cash
Beer	Delhi	5000
Beer	Punjab	4500
Beer	Haryana	6000
Wine	Delhi	6500
Wine	Punjab	7500
Wine	Haryana	5000
Vodka	Delhi	4500
Vodka	Punjab	2500
Vodka	Haryana	2000

If we modified the above data as dimensional point-of-view:

Product	Delhi	Punjab	Haryana
Beer	5000	4500	6000
Wine	6500	7500	5000
Vodka	4500	2500	2000

But it seems like both points of view would nonetheless be implemented in an identical star schema and a star schema really lies at the intersection of the relational model of data and the dimensional model of data. It's really a way of starting with a dimensional model, and mapping it into SQL tables that somewhat resembles the SQL tables you get if you start from a relational model.

Product Dim				State Dim
Product Id		Sales Fact		State Id
Product Name		Product Id		State Name
		State Id
		Sales Cash

The standard approach to relational data modelling is not fit for purpose for Business Intelligence workloads. This is because a relational data modelling is a combination of multiple tables and multiple joins but in dimension modelling, we force to use de-normalize multiple related tables into one table to analyze the data in a very sophisticated manner.