Notes on dataarchitecture.

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

Your Databricks bill is mostly DBUs. A lot of those DBUs run ETL that never needed Spark in the first place.

Praising the engineer who fixes everything at 2am rewards the exact dependency that eventually takes the platform down with them.

Streaming has a cost nobody puts in the business case: the weekly tax of running it after the demo works.

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.

Want expert eyes on your data architecture?

No pitch. An honest conversation about whether I can help, and what shape it would take if I can.