Differences between Kimball and Inmon

Discuss the essential differences between Kimball and Inmon approaches to DW development

The differences between Kimball and Inmon is given below:

Kimball

Ralph Kimball introduced the Kimball approach of data warehouse design.
Kimball uses dimensional models such as star schemas or snowflakes to organise the data in a dimensional data warehouse.
In the dimensional data warehouse of Kimball, analytic systems can access data directly. In Inmon’s architecture, analytic systems can only access data in an enterprise data warehouse via data marts.
Kimball’s architecture, it is unnecessary to separate the data marts from the dimensional data warehouse.
Kimball focus on rapid development, business user focus and is flexible for evolving business needs.

Inmon

Bill Inmon pioneered Inmon’s technique of creating a data warehouse design.
Inmon uses the ER model in an enterprise data warehouse. Inmon only uses a dimensional model for data marts only while Kimball uses it for all data.
In the dimensional data warehouse of Kimball, analytic systems can access data directly. In Inmon’s architecture, analytic systems can only access data in an enterprise data warehouse via data marts.
Inmon uses data marts as physical separation from enterprise data warehouses, and they are built for departmental uses.
Inmon focuses on integration of data, consistency, and providing a single source of truth across the business.

The table below gives the differences between Kimball and Inmon:

Aspect	Kimball’s Approach	Inmon’s Approach
Architecture Focus	Dimensional models (Star/Snowflake) for data warehousing.	Enterprise Data Warehouse (EDW) based on the Entity-Relationship (ER) model.
Data Models	Uses dimensional models for both data warehouse and data marts.	Uses ER model for the enterprise data warehouse and dimensional models for data marts.
Analytic Access	Analytic systems can directly access data in the data warehouse.	Analytic systems access data primarily through data marts derived from the EDW.
Data Mart Concept	Dimensional data warehouse doesn’t necessarily separate data marts.	Physical separation of data marts from the enterprise data warehouse, serving specific departmental needs.
Emphasis	Rapid development, business user focus, flexibility for evolving needs.	Integration of data, consistency, providing a single source of truth.
Strengths	Quick results, user-friendly, adaptable to changing business needs.	Data integration, consistency, maintaining data integrity across the organization.