The medallion architecture is a layered approach to designing modern data platforms. Instead of taking raw data directly from source systems and turning it immediately into reports, data progresses through three distinct layers: Bronze, Silver and Gold.
Each layer has a specific purpose. Bronze preserves the raw source data, Silver cleans and standardises it, and Gold prepares trusted, business-ready data for reporting and analytics.
This separation is important because it creates a more reliable, traceable and maintainable data platform. If a figure in a Power BI report looks wrong, for example, the data can be traced backwards through the Gold, Silver and Bronze layers to determine where the problem originated.
The medallion architecture is not exclusive to Microsoft Fabric, but Fabric provides an excellent platform for implementing the pattern because storage, data engineering, data warehousing and Power BI can operate within the same integrated ecosystem.
Why the Layered Approach Matters
One of the biggest problems with traditional data and reporting environments is that data transformation can become difficult to follow.
Data may be extracted from multiple operational systems, manipulated through different processes and eventually loaded into reports. When something goes wrong, identifying exactly where the problem occurred can become time-consuming.
The medallion architecture addresses this by separating the data journey into clearly defined stages.
Rather than treating data ingestion, transformation and reporting as one large process, each layer performs a specific role.
This provides several practical advantages:
- Data can be validated at different stages of processing.
- Problems can be isolated more easily.
- Transformation logic becomes easier to understand and maintain.
- Raw source data remains available if downstream processing needs to be rerun.
- Reporting models can be separated from operational source structures.
- New analytics and AI use cases can reuse trusted data rather than repeatedly returning to source systems.
From an operational perspective, this can significantly reduce risk.
If a transformation in the Silver layer fails, for example, the organisation does not necessarily need to extract all the data from its operational systems again. The original data remains available in Bronze, allowing the downstream processing to be corrected and rerun.
That ability to recover from individual layers is one of the reasons the architecture works so well for enterprise data platforms.
The Bronze Layer: Raw Data
The Bronze layer is where data first enters the platform.
Its primary purpose is to preserve data in a form that closely represents the original source.
Data might come from ERP systems, CRM platforms, finance systems, APIs, spreadsheets, operational databases, cloud applications or other business systems.
At this stage, the objective is generally not to make the data perfect. It is to capture and retain it.
Keeping this raw data provides an important safety net.
Imagine an organisation receives daily data from an operational system. Several months later, a business rule changes or a transformation problem is discovered.
If only the transformed version of the data has been retained, the organisation may need to return to the original system and extract historical information again.
With a properly designed Bronze layer, the original data is already available.
The organisation can correct the downstream transformation and reprocess the information without relying on the source system to reproduce historical extracts.
Bronze therefore provides both an ingestion layer and an important foundation for data lineage and recoverability.
The Silver Layer: Cleaned and Standardised Data
The Silver layer is where raw data begins to become useful across the organisation.
Data from Bronze is cleaned, validated and standardised so that it can be consistently interpreted by downstream systems.
Typical Silver-layer processes can include:
- correcting or managing data quality issues
- standardising dates and data types
- removing or managing duplicates
- applying consistent naming conventions
- validating records
- combining related datasets
- standardising codes and classifications
- applying agreed transformation and business rules
This becomes particularly important when information comes from multiple systems.
One system might represent a customer using a numeric identifier, another might use an account number and another might use a different naming convention entirely. Without standardisation, combining those systems into enterprise reporting becomes difficult.
The Silver layer creates a controlled point where those differences can be resolved.
It can also become an important foundation for advanced analytics and AI.
AI is only as useful as the information available to it. Giving AI tools access to inconsistent, poorly structured or unvalidated data can produce equally unreliable results.
A well-designed Silver layer provides cleaner and more consistent organisational data that can support reporting, analytics, machine learning and future AI use cases.
The Gold Layer: Ready for Reporting
The Gold layer is where data becomes ready for business consumption.
While Bronze focuses on preserving source data and Silver focuses on cleaning and standardising it, Gold is designed around how the organisation needs to analyse information.
This is where enterprise reporting structures such as fact and dimension tables can be created.
For example, an organisation might create dimensions for:
- customers
- employees
- products
- locations
- suppliers
- dates
These can then connect to fact tables containing measurable business activity such as sales, transactions, financial results or operational events.
The objective is to create consistent, reusable data structures that support reporting across the organisation.
Instead of every Power BI report independently calculating the same business metrics from operational systems, the Gold layer provides a governed foundation from which reporting and semantic models can be built.
This helps create a single source of truth.
When a KPI such as revenue, utilisation, production or employee turnover is defined consistently within the platform, different reports can reference the same governed data rather than independently recreating the logic.
Why Traceability Is the Real Advantage
One of the most valuable benefits of the medallion architecture is not simply that the data is organised into three layers.
It is that the architecture makes the journey of data easier to understand and troubleshoot.
Consider a Power BI dashboard showing an unexpected revenue figure.
In a poorly structured environment, determining why the number is wrong could require investigating reports, spreadsheets, transformation scripts and source systems.
With a properly implemented medallion architecture, the investigation can move systematically backwards.
First, determine whether the figure is incorrect in the reporting model.
Then examine the Gold layer.
If the Gold data is incorrect, examine the corresponding Silver data.
If the Silver data is also incorrect, compare it with the original Bronze record.
This makes it possible to identify the point at which the data changed and understand whether the issue originated in the source system, ingestion process, transformation logic or reporting model.
For organisations relying on analytics for operational and strategic decisions, that traceability is extremely valuable.
A data platform should not simply produce numbers. It should allow the organisation to understand where those numbers came from.
How the Medallion Architecture Works in Microsoft Fabric
Microsoft Fabric brings together many of the capabilities required to implement a medallion architecture within one analytics platform.
At the foundation is OneLake, Microsoft’s unified data lake for Fabric.
Rather than maintaining completely separate storage environments for different analytics workloads, Fabric allows data to be stored centrally and used across the platform.
A typical Fabric implementation can therefore use the medallion architecture to organise how data progresses through OneLake.
Data is first ingested into Bronze.
Data engineering and transformation processes then create standardised Silver datasets.
Business-ready Gold structures are subsequently created for enterprise reporting and analytics.
Power BI can then sit above these governed data structures to provide dashboards, semantic models and analytics to users.
The exact implementation will depend on the organisation’s requirements. The medallion architecture is a design pattern rather than a rigid template that must be implemented identically everywhere.
The important principle is separation of responsibilities.
Raw data should not be confused with cleansed enterprise data, and cleansed enterprise data should not automatically be treated as the final structure required for business reporting.
Each layer exists for a reason.
Getting the Architecture Right From the Start
The medallion architecture appears simple when represented as three boxes labelled Bronze, Silver and Gold.
The challenge is deciding what should happen inside those layers.
Organisations need to determine how data will be ingested, how historical information will be retained, which transformations belong in Silver, how business rules will be governed, how Gold models should be structured and how reporting will consume the resulting information.
Those decisions become increasingly important as more systems, reports and users are added to the platform.
A poorly designed architecture can still work initially, particularly when only a handful of data sources are involved. Problems tend to appear later as the environment grows and different teams begin relying on the platform.
Getting the architecture right from the beginning creates a much stronger foundation for future analytics.
At AGER BI, we design and implement Microsoft Fabric environments around scalable, governed data architecture rather than simply connecting systems to dashboards.
Our approach considers the complete data journey, from ingestion and storage through transformation, modelling, governance and Power BI, so that organisations have a platform they can continue building on as their data requirements evolve.
If your organisation is considering Microsoft Fabric or reviewing an existing Fabric environment, contact AGER BI for a free consultation to discuss the right architecture for your data platform.
