Cloud Era Data Warehousing Insights from Kimball and Inmon

Introduction

Ralph Kimball and Bill Inmon have dominated conversations around data warehouse design for over two decades. Their approaches shaped how organizations stored and analysed business data. Kimball championed a bottom-up, business-focused model, while Inmon advocated a top-down, enterprise-first approach.

However, as cloud data platforms like Amazon Redshift, Snowflake, and Google BigQuery have transformed how companies build and scale analytics, a big question arises: Which approach stands stronger in the cloud era- Kimball or Inmon?

Let’s unpack both methods and explore their relevance in today’s cloud-first world.

A Quick Refresher on Kimball and Inmon

The Kimball Approach

Ralph Kimball believed in designing a data warehouse by focusing on business processes first. His method is often called the dimensional model.

• The warehouse is built around star schemas or snowflake schemas, making data easy for business analysts to understand.
• Development happens incrementally, teams can deliver one subject area (like sales, finance, or marketing) at a time.
• Because it’s business-driven, Kimball’s model shows value faster, giving organizations usable data marts without waiting for the entire enterprise warehouse.

Think of Kimball’s approach as “start small, grow big.”

The Inmon Approach

Bill Inmon, often called the “Father of Data Warehousing,” proposed a top-down methodology. His method is often described as the Corporate Information Factory or third normal form (3NF) modelling.

• The enterprise warehouse is built first, storing data in a highly normalized format.
• Data marts for specific business functions can be created from this centralized, consistent foundation.
• This approach ensures that data is consistent and integrated across the whole enterprise, reducing duplication or silos.

Think of Inmon’s approach as “build the foundation first, then add the rooms.”

Strengths and Weaknesses of Both Approaches

The Cloud Era

The rise of cloud data warehouses and data lakes has reshaped the playing field. Platforms like Amazon Redshift, Snowflake, BigQuery, and Databricks remove many limitations that influenced Kimball and Inmon’s original thinking.

Some key changes include:

1. Scalability on demand – Cloud platforms handle large volumes of data without the performance issues older systems faced.
2. Cheaper storage – The cost of storing raw, semi-structured, or structured data in the cloud is much lower than in traditional databases.
3. Separation of storage and compute – Modern warehouses let you scale processing independently, making it easier to support multiple workloads.
4. ELT over ETL – Raw data can be ingested and transformed inside the warehouse at scale instead of heavy data transformation before loading.

These shifts mean both Kimball and Inmon ideas need a fresh lens.

How Kimball Fits in the Cloud?

Cloud-native tools make it easier to build dimensional models quickly. Analysts can create star schemas on top of raw data using transformation tools like dbt (data build tool) or AWS Glue.

Kimball’s business-first approach aligns well with agile cloud practices. Teams can ingest data into a warehouse, create dimensional models for high-priority business cases, and iterate.

In short:

• The cloud reduces the performance pain of dimensional models.
• Kimball’s faster delivery remains attractive, especially in fast-moving industries.

How Inmon Fits in the Cloud?

Inmon’s focus on consistency also finds new life in the cloud. With data lakes and lakehouse architectures, raw data can be stored in a normalized way before being transformed into marts.

Modern data governance, cataloging, and metadata tools (like AWS Glue Data Catalog or Databricks Unity Catalog) support Inmon’s vision of enterprise-wide consistency.

In short:

• Cloud storage makes central repositories more affordable.
• Enterprises with strict compliance needs still find Inmon’s method valuable.

The Hybrid Reality: Best of Both Worlds

In practice, most modern cloud architectures use a blend of both approaches. Here’s how:

1. Raw Zone (Inmon-like): Store raw, normalized, and integrated data in a central data lake. This ensures consistency and provides a foundation.
2. Business Zone (Kimball-like): Create dimensional models or star schemas in the warehouse for business reporting and dashboards.
3. Self-Service Zone: Allow analysts and data scientists to explore data freely without waiting on IT.

This hybrid approach balances the speed of Kimball with the discipline of Inmon.

Conclusion

In the cloud era, Kimball and Inmon have no clear winner. The rise of scalable, flexible cloud platforms has blurred the lines between their methods.

• Kimball’s approach excels when speed and agility are the priority, making it a strong fit for startups and fast-moving businesses.
• Inmon’s approach proves valuable where consistency, compliance, and governance are essential, such as in highly regulated industries.
• A hybrid model works best for most organizations, starting with a centralized raw data layer for consistency and layering business-friendly dimensional marts on top for agility.

Drop a query if you have any questions regarding Kimball or Inmon and we will get back to you quickly.

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