If you’re working with a modern data stack Medallion Architecture is one of those patterns that just makes your life easier. It gives structure to your pipeline and helps you (and your teammates) understand what’s happening at each stage of your data flow.
At a high level, it breaks down into three layers:
🥉 Bronze = Raw data
🥈 Silver = Cleaned and modeled data
🥇 Gold = Business-ready data
Here’s a breakdown of what each layer is doing and why it matters:
Bronze Layer: Raw, Unfiltered Data
This is your data’s landing zone. You’re ingesting it exactly as it comes in with no major transformations, no business logic applied.
What it looks like:
- Original format (JSON, CSV, logs, etc.)
- Contains errors, duplicates, messy fields
- Stored for traceability and reprocessing if needed
What it’s for:
- Acting as a single source of truth
- Letting you reprocess downstream if something breaks or changes
Example:
Raw IoT sensor data in JSON format, dumped as-is into cloud storage
Silver Layer: Cleaned and Structured
Here’s where most of the action happens. You’re turning raw chaos into structured, usable tables- fact and dimension style.
What it looks like:
- Cleaned, deduplicated, enriched
- Joins between datasets (ex: metadata added)
- Nulls handled, formats fixed
- Structured as clean tables in your warehouse
What it’s for:
- Creating reusable base datasets
- Reducing complexity for downstream consumers
Example:
Cleaned customer transactions, deduplicated and stored as Parquet files in a data lake.
Gold Layer: Business-Ready & Aggregated
This is the final layer- clean, curated, and ready to answer real business questions.
What it looks like:
- Pre-aggregated summaries and metrics
- Data products organized by business use case (sales, marketing, etc.)
- Often tailored per team or department
What it’s for:
- Decision-making, dashboards, machine learning models
- Giving non-technical users access to the data they need- without making them think
Example:
A monthly summary of total revenue by product and region, including KPIs like YoY growth.
Should You Use Medallion Architecture in Your Org?
Pros
- Scalable: Works great for large datasets in lake house environments
- High quality: Built-in cleaning and structure
- Traceable: Raw layer keeps a full audit trail
- Reusable: Silver and Gold datasets power many use cases
- Flexible: Works with structured and semi-structured data
Cons
- Data governance: You need solid controls and documentation at each layer
- Processing cost: More layers = more compute and storage
- Complexity: More moving parts to maintain and monitor