---
title: "Napoleon's Influence on the Modern Data Stack : hyperdimensional Analysis with Malloy"
description: "Explore how Napoleon's March visualization revolutionized data analysis, and discover Malloy's groundbreaking approach to multi-dimensional data exploration in modern tech."
categories: ["trends"]
keywords: ["Malloy","hyperdimensional data analysis","data visualization","Napoleon's March","semantic layer","modern data stack","data analytics","automobile recall data","Lloyd Tabb","data insights"]
ai_summary: "Explore how Malloy simplifies hyperdimensional data analysis, inspired by Napoleon's March visualization."
date: 2022-11-30
lastmod: 2026-07-30
canonical_url: https://www.tomtunguz.com/malloy-napoleon/
author: "Tomasz Tunguz"
---

[![image](https://res.cloudinary.com/dzawgnnlr/image/upload/tqrdro2n0ll3vklketh1.png)](https://res.cloudinary.com/dzawgnnlr/image/upload/v1669865044/tqrdro2n0ll3vklketh1.png)

Data visualization is a passion of mine. I remember reading Edward Tufte's book [The Visual Display of Quantitative Information](https://www.amazon.com/Visual-Display-Quantitative-Information/dp/1930824130) & stumbling across Charles Minard's "[Napoleon's March](https://res.cloudinary.com/dzawgnnlr/image/upload/v1669865044/tqrdro2n0ll3vklketh1.png)."

Most visualizations plot 2 dimensions. Napoleon's March encodes 6 : the geography of the terrain, the route & the direction of the army, the headcount of the troops, the temperature of the battlefield, & the time of year of Napoleon's doomed quest to conquer Russia. Click to enlarge it.

It's a hyperdimensional work of art.

When Looker founder Lloyd Tabb showed me his new project [Malloy](https://www.malloydata.dev/) I thought immediately back to Napoleon's March. 

Malloy makes hyperdimensional data analysis straightforward. It's a new type of semantic layer. An example helps explain Malloy's advances.

[![image](https://res.cloudinary.com/dzawgnnlr/image/upload/kx5z4tqssjzju2h1vg52.png)](https://res.cloudinary.com/dzawgnnlr/image/upload/kx5z4tqssjzju2h1vg52.png)

This is [automobile recall data in the US](https://malloydata.github.io/malloy-samples/wasm/#/recalls?page=query&model=recalls&source=recalls&query=++query%3A+recalls+-%3E+recall_dashboard&run=true&name=Recall+Dashboard&styles=%7B%22new_query%22%3A%7B%22renderer%22%3A%22dashboard%22%7D%7D) using [Malloy](https://malloydata.github.io/malloy/documentation/malloy_by_example.html). 

There are 7 dimensions shown here: the car maker, the historical recall total, the percent of recalls, recalls by year, recalls by type, the most recent recalls, & the biggest recalls by number of cars. 

Within each cell, you'll find different analyses. The by_type table subgroups by recall type. Meanwhile, the by_year_time_chart is a time series of recalls over the last 50 years. You can click on any data point to drill into it. 

That's what hyperdimensional means. Showing data by different axes: time, type, manufacturer, & establishing relationships across them.

Minard must have toiled for weeks to mosaic his magnum opus. Data analysts today could coalesce a dashboard similar to the one above but it would still take hours or days.

The auto recall visualization above requires [38 lines of Malloy code](https://malloydata.github.io/malloy-samples/wasm/#/recalls?page=query&model=recalls&source=recalls&query=++query%3A+recalls+-%3E+recall_dashboard&run=true&name=Recall+Dashboard&styles=%7B%22new_query%22%3A%7B%22renderer%22%3A%22dashboard%22%7D%7D) which encode the layout, the graphics, & the aggregations. Malloy abstracts away the complexity. 

We're in the Decade of Data. The Modern Data Stack has created many powerful abstractions to enable more insightful data analysis. [The semantic layer](https://tomtunguz.com/9-predictions-for-data-2023/) is an important component of that progress. That's where Malloy fits in.

Had Napoleon lived today [his famous quote about readers might have read:](https://www.goodreads.com/quotes/27871-show-me-a-family-of-readers-and-i-will-show) "Show me a family of [data analysts], and I will show you the people who move the world."