Introduction
In modern cloud environments—whether built on Azure, GCP, or AWS—data has become a strategic asset. However, without a clear structure, a data platform can quickly become complex, costly, and difficult to maintain. The Medallion architecture (Bronze / Silver / Gold) offers a simple and robust approach to organizing data flows, improving data quality, and facilitating data utilization. It has now established itself as a standard in many data projects, particularly in cloud-based analytics environments.
Why design a data data architecture?
A data architecture is not limited to storing information. It must ensure:
- Traceability → The ability to understand where data comes from, what transformations it has undergone, and where it is used (data lineage);
- Quality → Ensuring that data is reliable, consistent, and up-to-date. This includes controls, business rules, and monitoring mechanisms to prevent inconsistencies;
- Maintainability → The ease of updating, fixing, or improving pipelines and models without breaking what already exists;
- Scalability → The system’s ability to handle an increase in data volume, the number of users, or the complexity of processing without experiencing a decline in performance;
- Clarity of responsibilities → Explicit definition of who does what (collection, processing, disclosure, governance).
Without a clear structure, transformations pile up in a disorganized manner, intermediate tables multiply, and business logic becomes scattered across different tools. This situation quickly leads to a loss of control: difficulty understanding the origin of the data, inconsistencies in metrics, and increased dependence on the project’s original developers.
Structuring the architecture allows for a clear separation of ingestion, transformation, and exposure. This separation improves the overall readability of the system and makes it easier to evolve over time.
The concept of the Medallion architecture
The Medallion architecture is based on a simple principle: organizing data according to its level of maturity. Instead of directly transforming raw data into business indicators, the model introduces intermediate layers that allow the information to be progressively validated.
Simplified representation:
Each layer plays a specific role and serves a specific purpose. This approach is particularly common in data lakehouse environments, but it is entirely independent of the tools used.
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The layers of the Medallion architecture
🥉 Bronze - Raw Data
The Bronze layer consists of data ingested from source systems with minimal processing.
The main objective is to preserve the information in its original state in order to ensure:
- Traceability;
- The ability to replay treatments;
- History retention.
Beyond its role as a storage layer, this layer also plays a key role in managing data flows. It enables the monitoring of ingested data volumes, the archiving of source data, and the tracking of data loads using technical metadata. Minimal validations can be applied at this stage, primarily to ensure the technical integrity of the data (format, schema, etc.), without introducing business logic. The data is thus stored as-is, often in a data lake or a “raw” table.
🥈 Silver - Reliable Data
The Silver layer is at the heart of the data transformation process. Here, the data is cleaned, structured, and enriched. This includes, in particular:
- Data validation → Conversion of data to the appropriate formats;
- Handling missing values → Processing null or incomplete fields using defined rules;
- Deduplication → The process of identifying and removing duplicates from a dataset in order to retain only one “valid” record per business entity;
- Technical joins → Combining multiple source tables to reconstruct a complete entity (e.g., enriching an order with customer information);
- Application of basic business rules → Implementation of initial simple functional logic.
The goal is to produce consistent, stable, and reusable data. The Silver layer generally serves as the technical “source of truth” upon which future analyses are based.
🥇 Gold - Business-Driven Data
The Gold layer transforms verified data into information that business teams can use directly.
This layer includes, in particular:
- Aggregations → Groupings and summary calculations (sum, average, etc.) performed at a level relevant to the analysis, such as monthly revenue, for example;
- Key performance indicators (KPIs) → Strategic metrics defined by the business to monitor performance;
- Tables optimized for BI → Structured models designed for easy use in tools like Power BI: star schemas, fact tables, and dimension tables;
- Specific analytical models → Datasets prepared for specific use cases (marketing, finance, product, etc.).
This layer is designed for consumption, not for technical processing. It aligns the data with the company’s decision-making needs.
Advantages and limitations of the Medallion architecture
Benefits
The Medallion architecture offers several major benefits.
First, it provides a high degree of clarity. Each layer has a clearly defined role, which reduces ambiguity and facilitates collaboration between data, BI, and business teams. It also improves governance. Separating the transformation layers simplifies quality monitoring and data lineage. Finally, it naturally adapts to distributed cloud environments, where storage and computing evolve independently.
Limitations
However, this architecture is not a one-size-fits-all solution. It can lead to higher storage costs, as each tier stores a version of the data with a different level of processing. The Bronze tier typically stores all raw data, which requires careful management of retention policies and storage volumes. Furthermore, partial data duplication across tiers—particularly in the Gold tier—can exacerbate these costs.
If applied too rigidly, this architecture can also slow down certain projects that require greater flexibility. Finally, while it effectively structures data flows, it does not replace a genuine governance strategy or a clear division of responsibilities among teams.
When and how should you adopt it?
The Medallion architecture is particularly well-suited for the following scenarios:
- Multiple data sources → When a company collects data from multiple systems (CRM, ERP, APIs, files, etc.), requiring gradual structuring to standardize formats;
- Large data volumes → When data volumes are high or growing rapidly;
- Strong need for traceability → In environments where it is essential to be able to explain the origin of a metric or justify a figure;
- Collaboration among multiple teams → When data engineers, data analysts, data scientists, and business teams work together, requiring a clear separation of responsibilities and processing stages.
However, its adoption requires a clear framework from the outset. It is recommended that:
- Clearly define the role of each layer → Clarify the responsibilities of Bronze (raw data storage), Silver (cleaning and structuring), and Gold (business modeling) to avoid overlap or deviations;
- Establish naming conventions → Standardize table, column, and model names to improve readability, maintainability, and the onboarding of new team members;
- Automate data processing pipelines → Orchestrate data transformations to ensure data reliability and regular updates;
- Implement quality tests → Add automated checks (uniqueness, non-nullity, business consistency, etc.) to quickly detect anomalies;
- Documenting transformations → Describe the rules applied, the calculations performed, and the dependencies between models to ensure a better understanding across all teams.
The success of this architecture depends more on discipline and consistency than on the choice of technological tools.
Conclusion
The Medallion architecture now serves as a solid foundation for building a modern data platform. By clearly separating the transformation layers—from raw data to analytics—it enables data to be structured incrementally and made actionable. When properly implemented, it makes data more reliable, more traceable, easier to use, and geared toward creating business value. It does not replace a comprehensive data strategy, but it provides clean foundations—essential for building a scalable and sustainable platform. And in data, clean foundations make all the difference.
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What is Medallion architecture, in a nutshell?
The Medallion architecture is a three-tier data organization model: Bronze (raw), Silver (reliability-enhanced), and Gold (business-oriented). It enables the gradual transformation of raw data into actionable information, while ensuring traceability and quality.
Why has Medallion architecture become popular?
It addresses a common challenge faced by modern data platforms: the growing complexity of data flows and transformations. By organizing data by maturity level, it enhances clarity, governance, and scalability, particularly in cloud and Data Lakehouse environments.
Is the Medallion architecture required for a data platform?
No, but it is now a widely adopted best practice.
It is particularly relevant when volumes increase, teams expand, and governance requirements become more stringent.
How can data quality be ensured in a Medallion architecture?
Quality depends primarily on the silver layer.
It must include:
- Automated tests (uniqueness, non-nullity, business consistency);
- Schema checks;
- Monitoring;
- Clear documentation of the rules applied.
Architecture shapes workflows, but their quality depends on the practices implemented around it.
What is the main purpose of the Medallion architecture?
Its goal is simple: to organize the data so that it is reliable, understandable, and usable over the long term.
Nicolas Jacob Peres
Data Engineer
