Medallion Architecture

20 December 2024

In the current big data era, companies must effectively manage data to make data-driven decisions. One such well-known data management architecture is the Medallion Architecture. This architecture offers a structured, scalable, modular approach to building data pipelines, ensuring data quality, and optimizing data operations.

What is Medallion Architecture?

Medallion Architecture is a system for managing and organizing data in stages. Each stage, or “medallion,” improves the quality and usefulness of the data, step by step. The main goal is to transform raw data into meaningful data that is ready for the analysis team.

The Three Layers of Medallion Architecture:

• Bronze Layer (Raw Data):
  This layer stores all raw data exactly as it comes in without any changes or cleaning, preserving a copy of the original data for fixing errors or reprocessing when needed.
  Example: Logs from a website, sensor data, or files uploaded by users.
  
• Silver Layer (Cleaned and Transformed Data):
  The Silver Layer involves cleaning, organizing, and validating data by fixing errors such as duplicates or missing values, ensuring the data is consistent and reliable for analysis, such as removing duplicate customer records or standardizing dates in a database
  Example: Removing duplicate customer records or standardizing dates in a database.
• Gold Layer (Business-Ready Data):
  The Gold Layer contains final, polished data optimized for reports, dashboards, and decision-making, providing businesses with exactly the information they need to make informed decisions
  Example: A table showing the total monthly sales for each region

Advantages:

• Improved Data Quality: Incremental layers progressively refine data quality from raw to business-ready datasets
• Scalability: Each layer can be scaled independently based on specific business requirements
• Security: If you have a large team to handle, you can separate them by their level
• Modularity: The layered approach separates responsibilities, simplifying management and debugging
• Traceability: Raw data preserved in the Bronze layer ensures traceability and allows reprocessing when issues arise in downstream layers
• Adaptability: The architecture supports diverse data sources and formats, making it suitable for various business needs

Challenges:

• Takes Time: Processing through multiple layers can delay results
• Storage Costs: Storing raw and processed data requires more space
• Requires Skills: Implementing this architecture requires skilled data engineers familiar with ETL/ELT tools, cloud platforms, and distributed systems

Best Practices for Medallion Architecture:

• Automate ETL/ELT Processes: Use orchestration tools like Apache Airflow or AWS Step Functions to automate workflows between layers
• Enforce Data Quality at Each Layer: Validate schemas, apply deduplication rules, and ensure data consistency as it transitions through layers
• Monitor and Optimize Performance: Use monitoring tools to track pipeline performance and optimize transformations for scalability
• Leverage Modern Tools: Adopt cloud-native technologies like Databricks, Delta Lake, or Snowflake to simplify the implementation
• Plan for Governance: Implement robust data governance policies, including access control, data cataloging, and audit trails

Conclusion

Medallion Architecture is a robust framework for building reliable, scalable, and modular data pipelines. Its layered approach allows businesses to extract maximum value from their data by ensuring quality and consistency at every stage. While it comes with its challenges, the benefits of adopting Medallion Architecture often outweigh the drawbacks, making it a cornerstone for modern data engineering practices.