The Medallion Architecture (Batch)

Let’s be honest— “medallion architecture” sounds like something cooked up to make a slide deck sound cooler than it actually is. But I promise you this one’s actually worth taking the time to learn and implement.

At its core the medallion architecture is a simple, scalable way to organize your data into three layers: Bronze, Silver, and Gold. Yes, like Olympic medals and no you don’t get a podium, but you will get recognition if you implement it correctly and here’s why.

It’s not just about being neat. This structure helps you:

• Cut down on redundant processing

• Improve data quality and traceability

• Apply smarter governance and access controls

• Use your capacity more efficiently (which saves money and will get you that recognition I was talking about above)

Before we talk about how it makes your life easier, let’s walk through what each layer is actually for—and why they matter.

Bronze: The Raw Landing Zone

Welcome to the starting line (well for analytics at least).

The Bronze layer is where all your data lands first, untouched and unfiltered. Think of it as your inbox before any rules are applied—where everything piles up before you sort through it. It’s raw, messy, and that’s exactly the point. It’s a replica of your transactional system, excel files of every version of next fiscals budget, and that random json file someone needs for a data science project.

In Fabric, this usually lives in a Lakehouse, because it handles both structured (delta tables) and unstructured data (csv, parquet, excel).

So why keep things raw? Because Bronze is your system of record—a snapshot of your data exactly as it arrived. No filters. No transformations. Just full fidelity, ready for anything you might need later (like debugging that error that shows up once a month, or proving to your coworker that you were right and it’s the source system’s fault).

Here’s what makes Bronze Bronze:

• Raw by design – Stores whatever shows up: JSON, CSV, Parquet, you name it. No changes.

• Append-only – New records are added over time. Think of it as a historical log that you can always replay if something goes sideways.

• Not for analysis – This is not the layer your analysts should be querying. You should use it for validation though.

• Great for traceability – You’re keeping the original structure, which helps when you need to trace an issue back to the source.

• Flexible ingestion – Works with both batch and streaming sources—ADLS, S3, Kafka, Event Hubs, you get the idea.

In short, Bronze is the foundation. It’s the layer that lets you confidently say, “Yes, we have the original data—no, we didn’t accidentally overwrite it three months ago.”

Next up? We take that messy inbox and start cleaning it up.

Silver: Cleaned and Modeled

This is where things start to get interesting—and where, in my experience, most of the work actually happens.

Silver is the layer where you take all that raw, messy data from Bronze and start making sense of it. You clean it up, apply structure, and turn it into something the business can actually use. Think of it as the translation layer—you're taking “data” and turning it into “information.”

In Fabric, that usually means using Notebooks, Data Pipelines, or a mix of both to apply your business logic. Maybe you’re flattening nested JSON. Maybe you’re fixing timestamp formats. Maybe you're adding logic that finance swears is critical—kind of like how WeWork swore their “community-adjusted EBITDA” was a thing.

Here’s what Silver is all about:

• Transformation starts here – Filtering, joining, standardizing, deduplicating—this is where raw data starts becoming analysis-ready.

• Business logic lives here – Whether it’s calculating revenue, flagging status fields, or prepping a clean dimension table, this is your playground.

• Not quite final – It’s not ready for dashboards yet, but it’s miles ahead of where it started. Think: clean ingredients, not the final dish.

• Reduces pain later – Validating and standardizing early keeps things clean downstream—especially when people start building reports on top of it.

If Bronze is your raw transactional system duplicate, Silver is where the analytic layer starts to take form. It’s where you bring structure and logic into place.

But here’s the thing—your exec team probably doesn’t care about Silver. They want polished dashboards, KPIs, and numbers that “just make sense.” They don’t want to know how the hotdog is made – they just want the finished product. That’s where Gold comes in.

Gold: Business-Ready Insights

The Gold layer is your polished, analytics-ready data—this is the stuff your executives, analysts, and self-service heroes actually see. It’s where you take everything you’ve cleaned and modeled in Silver, and serve it up in a way that’s fast, clear, and business-friendly.

In Fabric, this usually means:

• Loading into Warehouse tables

• Building views with clean, readable column names

• Designing semantic models in Power BI

• Applying row-level security to make sure the right people see the right numbers (and only those numbers)

Unlike the granular detail in Bronze or Silver, Gold is typically aggregated—daily, weekly, monthly—whatever fits the business question.

This data is optimized for reporting and querying. Think that you’re serving it on a platter for your end users to consume. Theres no additional work for them to be done. You can attach AI models to it, create suites of reporting, even give access to power users for reporting.

Because the gold layer models a business domain, some customers create multiple gold layers to meet different business needs, such as HR, finance, and operations.

Use cases:

• Semantic Models

• Reports & Dashboards

• LLM Built on Clean Datasets

Final Thoughts (and a Quick Reality Check)

No architecture is a silver bullet. You still need good data practices, solid governance, and a team that understands the business. But Medallion gives you a head start. It gives your data a home, a purpose, and a path forward.