Kimball vs. Inmon — What's the Difference?

Kimball approach, developed by Ralph Kimball, emphasizes the dimensional modeling technique. It advocates for starting data warehouse design with business processes and building data marts for each process, which are then integrated. The Inmon approach, developed by Bill Inmon, is based on building a centralized, normalized data warehouse before any dimensional data marts, focusing on creating a single, consistent data model.

In the Kimball methodology, the focus is on delivering practical, usable data marts quickly that directly address business needs. This approach is often faster to implement and provides immediate value to business users. In contrast, the Inmon approach prioritizes a comprehensive data warehouse model that ensures data consistency and is more scalable in the long term but can be slower and more complex to implement.

Kimball's dimensional models are easier for business users to understand and navigate, as they directly align with business processes. However, Inmon's normalized model, while more complex, avoids data redundancy and maintains data integrity across the organization.

For integration, the Kimball approach integrates data at the data mart level, allowing for flexibility and quicker deployment. Inmon's approach centralizes integration at the data warehouse level, which requires more upfront work but leads to a more unified data environment.

Kimball is often preferred for smaller to medium-sized projects or those requiring quick, tangible results. Inmon's methodology, being more comprehensive and systematic, is suited for large-scale enterprises where long-term data consistency and scalability are crucial.