A 5-stage framework for effective data modeling and data warehouse architecture

When it comes to data modeling and data warehouse architecture, I’ve experimented with everything from anarchy to extreme governance. While there’s no one-size-fits-all approach, I’ve found a 5-stage framework that works remarkably well in most cases. It’s the same framework I often recommend to companies starting their data warehouse journey.

Let’s dive into each stage, which you can also think of as layers in your data warehouse:

1. Landing dataset (source data)

This is where it all begins: the raw dataset generated directly from a data source. It’s untouched and ready to feed into the next stages of transformation and analysis.

2. Raw layer

This layer consists of views only, created from the landing dataset. It’s designed to avoid making changes directly to the source data, ensuring you always have an original copy to reference. This layer is also your first point of defense when source data changes unexpectedly, allowing for quick fixes without breaking your workflows.

3. Staging layer

In the staging layer, we apply essential transformations, naming conventions, and standardizations such as currency conversions. Data cleansing and enrichment also happen here. However, the table structure remains unchanged, providing a clean yet faithful representation of your source data.

4. Core layer

The core layer is where company-wide definitions are established. This is critical for ensuring that all data models rely on the same general objects. Consistency is key here - this layer eliminates confusion and fosters a shared understanding of metrics across your teams.

For example, Danish Endurance implemented this approach to centralize their data, enabling them to analyze supplier costs effectively. This clarity allowed them to optimize pricing strategies, leading to a 77% increase in profitability.

5. Analytics layer

The final layer is built using the foundational blocks from the core layer. This is where you add new definitions and business logic tailored to specific dashboards. Any new definitions introduced here should be added to the core layer and meticulously documented. Finally, connect your BI tool to the analytics layer for visualization and insights.

Bringing it all together

Transforming raw data into actionable insights isn’t just a technical task - it’s a business imperative. Without a structured approach, teams risk "dashboard overwhelm," where conflicting metrics and poorly managed data lead to confusion and inefficiency.

This is why data transformation is critical. Platforms like Shopify, Facebook Ads, and Google Analytics generate valuable raw data, but in its initial state, it's often fragmented and inconsistent. Transforming this data into unified, meaningful metrics is the foundation for smarter decision-making.

How it compares to the Medallion Architecture

The Medallion Architecture, popularized by Azure Databricks, shares many similarities with this 5-stage framework. Both approaches emphasize layered data processing to improve data quality progressively:

• Bronze Layer aligns with the Landing and Raw Layers, focusing on ingesting and preserving raw data.

• Silver Layer mirrors the Staging Layer, emphasizing data cleaning, validation, and enrichment.

• Gold Layer aligns with the Core and Analytics Layers, focusing on refined, aggregated data tailored for analytics and business intelligence.

  
  

Source: Microsoft

While the Medallion Architecture is closely associated with data lakes and lakehouses, the 5-stage framework is a practical approach for any data warehouse setup, providing flexibility and scalability for diverse business needs.

Future-proofing your data operations

As your organization grows, your data strategy must evolve too. A well-structured framework ensures:

• Scalability: Easily integrate new data sources as your needs expand.
• Consistency: Maintain accurate metrics across dashboards and reports.
• Efficiency: Focus on strategic initiatives by reducing manual work and errors.

By regularly reviewing and updating your Core and Analytics layers, you can adapt to changing business requirements without disrupting workflows. This approach not only ensures clean, actionable data but also keeps your entire data ecosystem aligned with business goals.

Final thoughts

Whether you’re following the Medallion Architecture or this 5-stage framework, the goal is the same: to create a structured, scalable system that transforms raw data into actionable insights. By implementing these principles, you can future-proof your data strategy, foster team alignment, and drive smarter decision-making.

Which approach works best for your organization? Let us know!