2 minute read / Nov 30, 2022 /
Napoleon's Influence on the Modern Data Stack : hyperdimensional Analysis with Malloy
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 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.
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 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 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: “Show me a family of [data analysts], and I will show you the people who move the world.”