Medallion Architecture in Excel
The three-tab rule
I've worked in technology for a long time. In fact, I've had a 25-year long career in software and data, which means I've had to use a lot of different programming languages, frameworks, platforms and tech stacks. But it's also meant that I've used Excel a lot. I would describe myself as fairly competent in Excel. Not world championship level by any means, and probably not even Joel Spolsky level (although I did start to suck slightly less after watching this 10 years ago). But fairly competent.
However, I came across an interesting article recently that really grabbed my attention. This post on How-To Geek describes a "three-tab rule" in Excel, separating source data, logic, and presentation. It's framed by making comparisons to the Model-View-Controller (MVC) pattern in web development, something I'm very familiar with, but had never considered applying to Excel. I was pretty surprised that I'd never come across this before (especially as I've spent a lot of time thinking about engineering practices in software), but I was also immediately struck by how useful, sensible and simple it was.
The pattern described proposes structuring Excel workbooks using three distinct tabs: Source, Logic, and Interface. The premise is straightforward - most spreadsheets fail because they mix raw data, calculations, and final reports on the same screen. By separating these concerns, you create workbooks that are easier to audit, maintain, and scale.
The MVC-based pattern works like this: the Source tab holds your raw, unmodified data in a structured format (ideally as an Excel Table). The Logic tab does all the heavy lifting - transformations, calculations, lookups, using modern Excel functions like FILTER, SORT, and LAMBDA. The Interface tab presents the final, polished output that stakeholders actually see.
This resonates strongly with how we think about data architecture at endjin. But while the original article frames this through the Model-View-Controller pattern, I think there's a more relevant mental model from the data engineering world that fits this use case perfectly.
From MVC to Medallion
If you've worked with modern data platforms - whether that's Databricks, Microsoft Fabric, or Azure Synapse - you'll likely be familiar with the Medallion Architecture. As Carmel described in her recent deep-dive on the topic, it's a data design pattern that consists of three tiers: Bronze (raw), Silver (cleaned and validated), and Gold (projected for specific use cases).
The parallel to the three-tab Excel rule is obvious:
| Medallion Tier | Excel Tab | Purpose |
|---|---|---|
| Bronze | Source | Raw data in its original form. No transformations, no cosmetic formatting. A historical archive of what was received. |
| Silver | Clean | Cleaned, validated, and structured data. Schema standardisation. This is where raw data becomes useful information. |
| Gold | Output | Transformed and projected for a specific use case. Logic and calculations applied. Polished, formatted, and ready for consumption by stakeholders. This might be a pivot table, a chart, or another targeted, tabular presentation. |
This isn't just a semantic rebrand. The Medallion Architecture brings with it a wealth of best practices from the data engineering community - around data quality, validation, lineage, and governance that can inform how you approach your data workloads, even in Excel.
The benefits of this approach
When you start thinking about your Excel workbook as a miniature data pipeline, several good practices naturally follow.
Data lineage becomes visible. With clear separation, you can trace any value in your Output tab back through the Clean tab to its origin in the Source tab. When a number looks wrong, you know where to look.
Validation can be added systematically. The Clean tab becomes the natural place to add data quality checks - are there unexpected blanks, or duplicates? Do totals reconcile? Are values within expected ranges? This is the equivalent of creating quality gates as data moves from Bronze to Silver.
The Source tab becomes immutable. Just as the Bronze tier in a data lakehouse preserves raw data for historical playback, your Source tab should remain untouched. If new data arrives, append it rather than overwrite. This gives you an audit trail and the ability to reprocess if your logic changes.
Multiple projections from the same source. You might need different views of the same underlying data - one for the finance team, one for operations, one for the board. In the Medallion Architecture, this is exactly what the Gold tier enables. In Excel, you can create multiple Output tabs, all drawing from the same Clean layer.
Proceed with caution
I should be clear that whilst this pattern makes Excel workbooks more robust, more maintainable, and more professional, it doesn't make Excel the right tool for every job. I hold some strong opinions about when and where Excel is appropriate, and when the stakes are high - when significant decisions are being made and errors could impact financial, regulatory compliance, or even public health outcomes, Excel isn't enough. You more than likely need proper software engineering discipline and quality control barriers across technology, process, and people. This pattern doesn't prevent someone accidentally filtering and deleting rows in the Source tab. It doesn't stop formulas breaking silently. It doesn't provide version control, automated testing, or audit logs.
But, for those situations where Excel genuinely is appropriate, or where constraints mean it's your only option, this approach represents a genuine step-change in how you structure your work.
Summary
The three-tab rule for Excel is a pattern worth adopting. It has a direct comparison to the well established Medallion Architecture, connecting your Excel work to the broader principles of modern data engineering: separation of concerns, data quality, lineage, and fit-for-purpose projections.
Of course, this approach doesn't come without limitations. Excel remains a tool designed for individual productivity, not enterprise data management. If your workbook is becoming mission-critical - if it updates regularly, if others depend on it, if errors would be costly, then it's worth asking whether you've outgrown what Excel can safely provide.
