📘 Introduction

In the age of data-driven decision making, a well-structured and scalable data architecture is essential. The Medallion Architecture is a proven framework that organizes data into multiple layers of refinement — ensuring clarity, governance, and trust as data flows from raw ingestion to business-ready insights.

When combined with Databricks and Unity Catalog, it forms the backbone of a modern Lakehouse — a single platform for storing, processing, and analyzing all your data. In this post, you’ll learn how to set up the Medallion Architecture in Databricks, define your data layers under a catalog, and structure your data pipeline using Databricks notebooks.

🏅 What is the Medallion Architecture?

The Medallion Architecture divides data processing into layers of increasing quality and business value. Each layer refines the data further, improving consistency, transparency, and usability across the Lakehouse.

🥉 Bronze Layer — Raw and Standardized

The Bronze layer captures raw data from diverse sources — APIs, files, databases, or streaming systems. It serves as the foundation of your data pipeline, preserving the original data in a standardized format as-is.

💡
All Bronze data is stored as Delta tables, enabling versioning, ACID transactions, and schema evolution.

🥈 Silver Layer — Cleaned and Validated

The Silver layer refines the data by cleaning and validating it. Duplicates, missing values, and inconsistencies are addressed here to produce high-quality datasets ready for analysis or further transformation.

💡
This layer also uses Delta tables, ensuring reliability and efficiency for updates and merges.

🥇 Gold Layer — Business-Ready Data

The Gold layer contains the final, curated datasets used for analytics, dashboards, and machine learning. It represents the highest level of trust and usability.

💡
Gold tables are also stored in Delta format, providing performance and scalability for business consumption. For reporting and dashboarding, Gold tables are typically structured in a star schema.

✅ Prerequisites

Before starting, make sure you have the following:

☁️☑️ Access to a Databricks workspace
📁☑️ Unity Catalog enabled
🔑☑️ Permission to create catalogs and schemas

💡
For a complementary approach, you can also see how to set up the Medallion Architecture using dbt in a separate guide.
Set up Medallion Architecture with dbt: From Raw Data to Gold Standard
Introduction In the age of data-driven decision making, having a powerful data architecture is crucial. The Medallion Architecture is a proven data architecture pattern that helps in organizing data across different layers of refinement. When combined with dbt (data build tool), it becomes a powerful and scalable way to manage
Deep Learning Nerds | The ultimate Learning Platform for AI and Data ScienceData Engineer

🗂️1️⃣ Structure Your Unity Catalog

To set up the Medallion Architecture in Databricks, we’ll use Unity Catalog — Databricks’ unified governance layer for data, AI, and analytics. Unity Catalog organizes all data objects in a clear hierarchy:

catalog.schema.table

In this setup, we’ll define:

  • Catalog: dlnerds (use a short project prefix or name that identifies your domain)
  • Schemas:
    • bronze — for raw data
    • silver — for cleaned data
    • gold — for business-ready data

Your Lakehouse structure could look like this:

You can view this post with the tier: Academy Membership

Join academy now to read the post and get access to the full library of premium posts for academy members only.

Join Academy Already have an account? Sign In