Inmon works with the normalized data model, whereas Kimball prefers the denormalized data model, and as such, we find redundant data models present in the Kimball architecture. The design and architecture of Inmon can be complex, but Kimball based data warehouses are easier to design and implement.
According to Inmon, data should be fed directly into the data warehouse straight after the ETL process. Kimball, however, maintains that after the ETL process, data should be loaded into data marts, with the union of all these data marts creating a conceptual (not actual) data warehouse.
Functions of A Data Warehouse
Data warehouse functions as a repository. It helps organizations avoid the cost of storage systems and backup data at an enterprise level Normalization vs. Denormalization Approach
Normalization is defined as a way of data re-organization. This helps meet two main requirements in an enterprise data warehouse i.e The Two Data Warehouse Concepts: Kimball vs. Inmon
Both data warehouse design methodologies have their own pros and cons. Let’s go through them in detail to figure out which one is better Which Data Warehouse Approach to Choose?
Now that we’ve evaluated the Kimball vs. Inmon approach and seen the advantages and drawbacks of both these methods Bottom-Line
Both Kimball vs. Inmon data warehouse concepts can be used to design data warehouse models successfully. In fact Astera Data Warehouse Builder – An Automated Data Warehousing Solution
Astera Data Warehouse Builderoffers an integrated platform to design 