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Inmon VS Kimball Approach in Data Warehousing

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


Organisations can utilise data warehouses to integrate data, automate data operations, and employ the central repository to support all reporting, Business Intelligence(BI), analytics, and decision-making across the organisation.

However, creating a data warehouse architecture might be difficult. The design patterns involved in developing and managing the data warehousing environment can be complicated, especially concerning the scaling, the total cost of ownership, flexibility, and accessibility.

The Inmon and Kimball approaches are the primary strategies for data warehouse architectural design.

In this article, we'll dig deeper into what each author offered, underline the relative cons of each strategy, and compare and contrast the two techniques.

Inmon Approach

The Inmon approach is known as the top-down or data-driven strategy, in which we start with the data warehouse and break it down into data marts. These data marts are then specialised to satisfy the demands of other departments inside the firm, such as finance, accounting, and human resources.

Bill Inmon offered a top-down approach to data warehouse architecture which is known as the Inmon approach:

  • Begin with the corporation's data model. Identify all of the data sources that the company has access to. 
  • Identify the essential entities (customer, product, order, etc.) and their respective linkages based on the data and understanding of business needs.
  • Create a thorough, logical model using the entity structure. The logical model includes all of the properties of each entity, as well as their respective interactions and codependencies, in great detail. According to data modelling terminology, the logical model creates logical schemas for entity relationships.
  • Build the physical model from the logical one. Extract data from various sources, alter it and integrate it into a normalised data model using ETL operations. Each normalised data model stores data in the third normal form to avoid redundancy. The data warehouse's core is the normalised data model.
  • Create data marts for different departments. For all reporting needs, data marts are used to access data, and the data warehouse serves as a single source of truth.


Inmon's architecture has several advantages.

  • Flexibility: Inmon's approach adapts faster to changing business needs and data source alterations. Inmon's architecture is more versatile due to the ETL process design that results in normalised data. The architects alter only a few normalised tables, communicating the modification downstream.
  • Single source of truth: Because of the normalised data model, the data warehouse serves as a single source of truth for the entire organisation.
  • Less prone to errors: Because normalisation minimises data redundancy, both engineering and analytical procedures are less susceptible to errors.
  • Completeness: Inmon's approach incorporates all Enterprise data, ensuring that all reporting requirements are met.


Inmon's architecture has some drawbacks.

  • Cost of initial setup and regular maintenance: The time and cost required to set up and maintain Inmon's architecture are far greater than the time and investment needed for Kimball's architecture. Normalised schemas are more challenging to build and maintain than their denormalised counterparts.
  • Skill requirement: Highly skilled engineers are required for Inmon's method, which is harder to come by and more expensive to maintain on the payroll.
  • Extra ETL is required: Separating data marts from the data warehouse necessitates the employment of more ETL processes to generate the data marts, resulting in increased engineering overhead.
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Kimball Approach

The Kimball approach is called bottom-up because we start with user-specific data marts, which are the core building blocks of our conceptual data warehouse. It's critical to know which model best meets your needs from the start; so that it can be incorporated into the data warehouse structure.

Ralph Kimball was the first one to introduce the Kimball approach. The Kimball method is based on a bottom-up approach:

  • Begin by identifying and documenting the most significant business operations, demands, and queries that are being asked.
  • All data sources available across the organisation should be documented.
  • Create ETL pipelines that gather, modify, and load data into a denormalised data model from data sources. The dimensional model is constructed in the form of either a star schema or a snowflake schema.
  • The dimensional model has frequently constructed around and within dimensional data marts for specific departments.


Kimball's architecture has several advantages.

  • Simplicity and speed: Kimball's architecture is significantly easier and faster to construct and establish. 
  • Understandable: Non-technical and technical staff both may understand the dimensional data model.
  • Relevancy: Kimball's bottom-up methodology, unlike Inmon's, makes all data linkages relevant to the business needs.
  • Engineering team needs: In comparison to Inmon's technique, Kimball requires fewer engineers with less specific technical abilities to set up and operate the data warehouse.


Kimball's architecture has some drawbacks.

  • Data redundancy: There is more data redundancy and hence a higher likelihood of errors since data is fed into a dimensional model.
  • No single source of truth: Data marts are used to design and organise data in the data warehouse. When combined with data redundancy, Kimball's architecture prevents the company from having a single source of truth.
  • Less adaptable: Kimball's architecture is less flexible and adaptable to modifications when data demands change, business requirements vary, and incoming data sources alter their payloads. 
  • Incomplete: The strategy taken begins (and concludes) with important business processes. As a result, it does not provide a complete 360-degree picture of business data. Instead, it helps report on particular subject areas in the corporate world.

Also see, Difference Between Analog and Digital Computer

Comparison Between Inmon Approach and Kimball Approach

By comparing and contrasting the advantages and disadvantages of these two approaches, one can differentiate between them. Let us see where each of these approaches has its strengths in. 

Kimball is the superior option if one wants to see results quickly, have a small team of engineers, and expect slight changes in business needs. Otherwise, data redundancy may result in anomalies and increased maintenance expenditures in the future.

Even if the data warehouse deployment will cost more and take longer than Kimball's counterpart, Inmon is the go-to for large organisations who want to see a complete picture of their corporate data.

Also read, Clean Architecture

Frequently Asked Questions

What is a data warehouse?

A Data Warehouse (DW) is a relational database built for query and analysis rather than transaction processing. It covers historical data generated from single and numerous sources of transaction data. A Data Warehouse delivers integrated, enterprise-wide historical data and focuses on data modelling and analysis tools for decision-makers.


Define ETL.

ETL stands for Extract, Transform, and Load, and it is a Data Warehousing procedure. An ETL tool extracts data from numerous data source systems, transforms it in the staging area, and then loads it into the Data Warehouse system.


What are data marts?

A data mart is an easy form of a data warehouse that focuses on a single subject or business line. Data Marts are analytical record stores focused on specific business functions for a particular community inside an organisation.


Why is Inmon Approach also known as CIF Approach?

The Inmon method is also known as the Corporate Information Factory or CIF because it begins with the data that an organisation has.


In this article, we learned about the Inmon approach and the Kimball approach. We saw the features of each approach. We also looked at their advantages and disadvantages. In the end, we found which is better suited according to the organisation’s needs.

We hope this blog has helped you enhance your knowledge. If you want to learn more, check out our articles on Data Warehousing - Coding Ninjas Coding Ninjas StudioData Warehousing Tools - Coding Ninjas Coding Ninjas StudioInmon Approach In Data Warehouse Designing - Coding Ninjas Coding Ninjas StudioKimball Approach In Data Warehouse Designing - Coding Ninjas Coding Ninjas Studio and Snowflake - Coding Ninjas Coding Ninjas Studio. Do upvote our blog to help other ninjas grow.

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Topics covered
Inmon Approach
Kimball Approach
Comparison Between Inmon Approach and Kimball Approach
Frequently Asked Questions
What is a data warehouse?
Define ETL.
What are data marts?
Why is Inmon Approach also known as CIF Approach?