If you’ve explored or worked with modern data management strategies, you’ve likely encountered terms like data lake, data warehouse, and the increasingly popular concept of the data lakehouse. These foundational ideas often intersect with frameworks like medallion architecture, which organize and optimize data processing across different layers. Let’s take a closer look at these concepts to set the stage for understanding their relevance in today’s data ecosystems.
The medallion architecture (or multi-hop architecture as some people call it) is an increasingly popular framework for organizing and processing data in modern data platforms. Designed to handle the complexities of today’s data environments, it offers a structured approach that simplifies data management while improving efficiency, quality, and usability. When transitioning from ETL to ELT, it’s a great framework to implement for efficient data management.
Bridging Data Lakes and Warehouses
While data lakes excel at storing raw data, they often lack the consistency and governance required for business intelligence and operational reporting. This is where data warehouses shine, offering well-structured and cleansed data ready for analysis. However, building and maintaining a data warehouse can be resource-intensive.
Enter the data lakehouse: a hybrid solution combining the scalability of a data lake with the structured reliability of a data warehouse. By storing raw data in a lake and curated, transformed data in a warehouse-like layer, you can achieve both the flexibility needed for data science workflows and the rigor required for business reporting. This unified approach bridges the gap between raw data storage and actionable insights.
ETL vs. ELT – Transforming Data for Insights
Central to working with data lakes and lakehouses are the concepts of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). Both processes describe how data is ingested, but the sequence of operations differs significantly:
- ETL: Data is extracted from the source, transformed into a predefined schema or format, and then loaded into the destination storage. This method requires a clear understanding of the target state before transformations can be designed.
- ELT: Data is first loaded into the target system in its raw format and transformed only when needed. This approach aligns well with modern architectures like data lakes, which emphasize storing raw data and applying transformations dynamically for specific use cases.
As data volumes grow and architectures evolve, ELT has become the preferred choice in many scenarios. Its ability to work with raw data and delay transformations until they are needed works very will with the principles of scalability and flexibility central to data lake-based ecosystems.
What is Medallion Architecture, and Why Does it Matter?
At its core, the medallion architecture breaks down your data lakehouse into three distinct layers: Bronze, Silver, and Gold. Each layer represents a different stage of data refinement, ensuring data is organized and processed in a systematic way.
- Bronze Layer: The Bronze layer is the entry point for raw data. This data is ingested from various sources – whether it’s transactional systems, IoT devices, APIs, or logs. The data here is unprocessed and may include duplicates, errors, and inconsistencies. The main purpose of this layer is to act as a storage area in your data lake for the raw data, preserving its original form for future reference or processing.
- Silver Layer: The Silver layer focuses on data cleaning and transformation. Here, the raw data is refined to remove duplicates, correct errors, and apply standard formats. Data quality checks are implemented to ensure accuracy and consistency. This layer produces a curated dataset that’s easier to query and analyze.
- Gold Layer: The Gold layer is where the final, business-ready data resides. This data is aggregated, enriched, and optimized for specific use cases such as reporting, dashboards, or advanced analytics. The Gold layer enables decision-makers to derive insights with confidence, as it represents the highest-quality version of the data.
Why Use Medallion Architecture?
The medallion architecture provides several benefits that make it a preferred choice for modern data platforms. One of the key advantages is scalability. By separating raw, intermediate, and refined data into layers, the architecture allows for easier scaling of storage and compute resources as data volumes grow. It also enhances data quality by incorporating quality checks and validation steps at each stage, ensuring that only accurate and consistent data progresses through the pipeline.
The architecture’s flexibility is very clear in the Bronze layer, which acts as a historical archive, allowing teams to revisit raw data to fix issues or implement new transformations in the future and makes historical analysis dependent on slowly changing dimensions a breeze. When you’re essentially building a change log for your data, it also allows for easy time-travel.
The silver layer comprises cleaned and incrementally loaded tables derived from the raw data. These silver tables act as an intermediate stage, ensuring that data is both structured and refined for further transformations and analyses. They serve as a bridge between the raw, unprocessed data in the bronze layer and the enriched, business-ready data in the gold layer.
Lastly, the Gold layer’s focus on business-ready data aligns the architecture closely with organizational goals, making it easier to generate actionable insights.
Best Practices for Implementing Medallion Architecture
To get the most out of the medallion architecture, it is important to adopt certain best practices. Automating ingestion and processing tasks using modern data tools and platforms can significantly reduce manual errors and speed up processing. Strong governance is also crucial, as clear guidelines for data access, security, and versioning across all layers help minimize risks and promote trust in the data.
Optimizing for performance through strategies such as indexing, partitioning, and caching can improve query performance, especially in the Silver and Gold layers. Continuous monitoring and validation of the data pipeline are necessary to detect errors, inconsistencies, or bottlenecks, ensuring high data quality is maintained.
Finally, leveraging cloud platforms like Azure, Google Cloud or AWS can simplify deployment and management, as these services offer native tools designed specifically for medallion architecture.
Conclusion
The medallion architecture provides a logical and efficient way to organize data in modern platforms. By breaking the pipeline into Bronze, Silver, and Gold layers, it ensures data is managed in a scalable, high-quality, and business-focused manner. Whether you’re a data engineer building pipelines or a business analyst relying on insights, the medallion architecture creates a solid foundation for success in the data-driven world.
