Introduction

In recent years, Medallion Architecture has garnered significant attention in the realm of enterprise data management. Initially created by Databricks, it was integrated into Microsoft's One Lake as the foundational guideline for its newly launched data platform, Microsoft Fabric, in November 2023. As Microsoft positions Fabric as central to its data solutions, the relevance of Medallion Architecture within their framework is expected to grow in the coming years.

What exactly is Medallion Architecture? What issues does it resolve, and how can it be effectively implemented? This article aims to provide insights into these queries, grounded in the principles that underpin this architecture along with general implementation guidelines.

Quick Overview of Data Lakes and ELT

If you're familiar with Medallion Architecture, you're likely aware of terms like data warehouse, data lake, and data lakehouse. Medallion Architecture is often discussed in relation to these concepts. The following sections offer a brief overview of these terms to establish a common understanding of Medallion Architecture.

A data lake serves as a centralized storage solution that accommodates both structured and unstructured data at any scale. Unlike traditional data storage options, data lakes are designed to keep data in its raw format—whether structured like relational databases, semi-structured like JSON or XML, or unstructured such as text documents, images, and videos. This allows for significant data storage from varied sources without the necessity of prior structuring.

One of the primary benefits of a data lake is its adaptability. Users can store data without concerns about structure or format, making it ideal for diverse data types intended for analytics, machine learning, or exploratory analysis. Additionally, data lakes typically support multiple processing frameworks and tools for querying, analyzing, and deriving insights from the stored data.

Closely associated with data lakes is the concept of a data lakehouse. Data in lakes often requires cleansing and structuring before it can be utilized by business users. Although data lakes store data in its raw format, they often lack the consistency and integrity needed for accurate business reporting. Conversely, data warehouses offer this functionality but demand greater investment and effort to establish. A data lakehouse combines the strengths of both, storing raw data in a lake while maintaining cleansed data in a warehouse, thus achieving scalability, flexibility, and analytical capability.

Related terms include ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). The acronyms represent similar processes but differ in their sequence. ETL transforms data into a predefined format prior to loading into the target system, whereas ELT loads data into the destination first, transforming it only as needed. The ELT approach is often favored in modern data engineering architectures, especially with the increasing reliance on data lakes that handle vast amounts of raw data.

The Challenge of Organizing Data in a Data Lake

However, the rise of data lakes and ELT processes has introduced a pressing issue: How should data be organized within these lakes, and how should ELT processes be designed accordingly? Traditional data warehouses are generally structured in three layers:

1. Staging Area: This serves as temporary storage where data is held during the ETL process, allowing for validation, transformation, and error handling before loading into the data warehouse.
2. Data Warehouse: This is the main repository for structured data, typically consisting of a relational database optimized for analytical queries. Data in warehouses is organized by business subject areas.
3. Data Mart: A subset of the data warehouse focused on specific business functions or departments, designed for easier access and analysis by targeted user groups.

In this traditional setup, the data mart layer contains analytics-oriented data, while the warehouse holds cleansed data organized by business subject areas. The staging area contains temporary data generated during the ETL process.

As we transition to a data lake environment, challenges arise. Data lakes store all raw data in its original format, which complicates the analysis process since raw data is not typically suitable for direct analytical purposes. Cleansing, transforming, and aggregating this data is often necessary. If one adheres strictly to the ELT concept, transformations occur only during report generation, which can be resource-intensive. Thus, there is a tendency to save intermediate transformation results in reusable formats, leading to questions about the boundaries of processing.

There are numerous operations—such as joins and aggregations—between the raw data and the final reporting-ready data. Attempting to save results from every transformation step could lead to an unmanageable number of intermediate files, many of which may soon become irrelevant. This can create a chaotic environment filled with unusable data.

To address this challenge, a framework is needed to clearly define the state of data, particularly intermediate data, and determine how to organize it. This is where Medallion Architecture comes into play.

What is Medallion Architecture?

Medallion Architecture serves as a blueprint for organizing data within a lakehouse framework. Its main objective is to enhance data structure and quality progressively as it moves through the Bronze, Silver, and Gold layers.

Different Data Layers in a Medallion Architecture

Understanding the components of Medallion Architecture clarifies how it can assist in organizing data lakes. The Bronze and Gold layers are generally well understood: the former contains raw data and metadata, while the latter functions similarly to the data mart layer in traditional data warehouses, housing consolidated data for reporting.

The Silver layer, however, deserves deeper consideration. By definition, it contains transformed data, preparing it from various sources into a unified view for further transformation into the Gold layer. The typical data engineering approach for the Silver layer is ELT, which applies minimal transformations during the loading process. Key tasks include:

• Data Cleansing: Identifying and rectifying errors and inconsistencies in datasets to enhance their quality and reliability for analysis.
• Data Verification: Ensuring data accuracy and consistency through various validation techniques, including cross-referencing and applying business rules.
• Data Conforming: Standardizing data to meet specific requirements, facilitating integration from diverse sources.
• Data Matching for Integration: Consolidating data from various sources to create a consistent enterprise view, often by generating universal primary keys.

Once these tasks are completed, the data in the Silver layer becomes meaningful and ready for integration into the Gold layer. A recurring question is whether further transformations should occur in the Silver layer. Generally, without clear indications that a transformation would be useful in multiple contexts, it is advisable to avoid unnecessary work. Specific transformations should be reserved for the transition from the Silver to the Gold layer.

The Silver layer is often compared to the ODS and DWH layers in traditional architectures. While they share similar purposes—collecting and preserving enterprise data—they differ in their methodologies. A data warehouse is organized by subject areas, while the Silver layer retains the original schema with minimal transformations. The ODS typically involves more extensive integration efforts.

The General Design Pattern of Medallion Architecture

Medallion Architecture is a design pattern aimed at improving data organization, rather than a specific data model. It accommodates various modeling principles, including dimensional, data vault, or relational models. Consequently, systems built using Medallion Architecture can incorporate files and tables based on any of these principles. This architecture can be applied across various data management ecosystems, including data warehouses, lakes, and lakehouses.

As previously mentioned, data lakes consist of raw format files that are cost-effective and flexible, while data marts often adopt Kimball-style star schemas for better analytical performance. Therefore, the Bronze layer typically resides in data lakes, while the Gold layer, which addresses reporting needs, may be housed in a dimensional data warehouse or a data lake.

The Silver layer offers flexibility in data modeling, allowing for structures resembling third-normal form, data vaults, or even NoSQL schemas. This variety means the Silver layer is often based in a data lake, which supports diverse modeling approaches. Thus, Medallion Architecture is frequently implemented in lakehouse environments, utilizing a data lake for the Bronze and Silver layers and a data warehouse for the Gold layer.

It is also feasible for Medallion Architecture to span multiple cloud vendors. For instance, the Bronze layer could exist across different clouds, aggregating data from various applications, while a specific cloud could be used for building the Silver layer based on the preferred transformation tools. Multiple Gold layer datasets could also be established across clouds for distinct reporting needs, creating a multi-cloud data platform that fits seamlessly within the Medallion Architecture framework.

Overall, Medallion Architecture provides a well-structured overview of enterprise data by classifying it into different layers (Bronze, Silver, Gold), catering to the needs of various stakeholders, from raw data ingestion to consumption-ready formats for analytics and reporting. This approach offers the adaptability required by the dynamic nature of business and allows for incremental improvements to the data ecosystem. Crucially, it establishes a strong foundation for data governance and compliance. By organizing data according to its maturity and defining processing boundaries for each layer, managing metadata, tracking lineage, auditing DataOps, and ensuring integrity and regulatory compliance become more straightforward. Improved organization leads to enhanced utilization and management, enabling Medallion Architecture to reduce costs, simplify management, and boost efficiency.

Final Thoughts

As a cloud solution architect, I am currently implementing Medallion Architectures for various clients. There are numerous opportunities and challenges, particularly as organizations transition to cloud solutions and embrace multi-cloud environments. The flexibility of Medallion Architecture makes it an ideal choice for these scenarios. If you would like to discuss Medallion Architecture, data lakehouses, or any related topics in cloud or data management, please feel free to share your thoughts in the comments. I look forward to your valuable feedback!