Notes on dataarchitecture.

Short essays on architecture, cost, hiring and AI: one cartoon, one idea, most days of the week. No tutorials, no listicles.

Investor technical due diligence has one real question: will this stack survive the growth the round is funding?

A wrong data engineering hire runs you about three salaries by the time you count the ramp, the re-hire, and the work that stalled in …

You bought a semantic layer to end the "which number is right" argument. Eighteen months later you have 200 metrics and the same argument, …

Most data teams know their monthly cloud bill. Far fewer can tell you which euro produced value and which one was pure waste.

The 2026 data tooling is the best it's ever been. The bottleneck in most teams I see is still a leader who can't say, in two sentences, what …

Run your AI feature twice on the same input. If a different answer would be a problem, you've found a deterministic requirement.

Most teams collect everything "just in case," then try to add privacy when an audit looms. By then it's in 40 tables.

An e-commerce client's reporting ran on a spreadsheet one person updated by hand every morning. It broke every quarter, always at month-end.

The best data engineers move for ownership and a manager who can describe the strategy. A 10% bump alone rarely does it.

The team bought more tokens. The agent still gave confident, wrong answers. The bottleneck was never compute.

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