Demystifying Medallion Layers in Snowflake: Best Practices and Common Pitfalls

Demystifying Medallion Layers in Snowflake: Best Practices and Common Pitfalls

As I delved into the world of Snowflake and Medallion layers, I realized that there’s a lot to learn and even more to unlearn. In this post, I’ll share my experience with Medallion layers and the lessons I learned the hard way.

## The Medallion Layer Architecture
Medallion layers, also known as the Bronze, Silver, and Gold architecture, is a popular design pattern in Snowflake. It’s meant to provide a structured approach to data warehousing, with each layer serving a specific purpose. The Bronze layer is for raw, unprocessed data, the Silver layer is for transformed and cleansed data, and the Gold layer is for aggregated and modeled data.

## The Problem: Views Across Layers
In my case, I encountered an issue where views in the Gold layer couldn’t access data from the Silver layer. I thought this was a fundamental aspect of Medallion layer architecture, but it turns out it’s not as straightforward as I thought.

Snowflake’s response was that Medallion layers are designed to be separate and don’t allow for views to access data across layers by design. This led me to wonder if I was missing something obvious.

## Best Practices for Medallion Layers
After digging deeper, I realized that there are a few best practices to keep in mind when working with Medallion layers:

– **Clearly define your requirements**: Make sure you understand the requirements of your project and communicate them clearly with your Snowflake RSA team.
– **Plan your architecture**: Take the time to plan your Medallion layer architecture and ensure that it meets your needs.
– **Test and iterate**: Test your views and data flows across layers to ensure that they work as expected.
– **Document everything**: Keep detailed documentation of your architecture, requirements, and testing to avoid confusion down the line.

## Conclusion
Medallion layers can be a powerful tool in Snowflake, but they require careful planning and execution. By following best practices and avoiding common pitfalls, you can ensure that your data warehousing project is a success.

*Further reading: [Snowflake Medallion Layers](https://docs.snowflake.com/en/user-guide/data-warehousing-architecture.html)*

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