Last week I wrote that most of your real value lives in pandas, SQL, and knowing where to look.
Almost every reply picked out the same word.
Is SQL still worth learning in 2026?
I get where the question comes from. SQL is what you learn in week two of a bootcamp. No release cycle, no conference, no version number anyone argues about. It looks finished.
So people treat it as the step you graduate out of.
Then this happens.
Earlier this year, I was brought onto an analytics project that was already finished. Several data sources merged, dashboards built, KPIs live and showing big revenue increases across almost every indicator. The provider had generated most of the SQL with AI and shipped it straight through. The queries ran. Nobody had checked the results.
Once you read them, the joins were wrong. Any record matching more than once duplicated its rows, and every KPI sitting on top was counting the same revenue several times over.
The growth was not growth. It was duplication.
Not because anyone was careless. Reading a hundred lines of someone else’s SQL and spotting where the grain breaks is a skill. It’s also the exact skill everyone assumes they can skip now.
Generation got cheap. Verification did not.
That’s the whole argument, and it cuts the opposite way from how people use it.
Everything above the data moved in three years.
The modeling layer.
The orchestration layer.
The framework you were told to learn in 2023 and now maintain out of obligation.
Underneath all of it, the data sat where it has always sat. In tables, behind a query.
Nobody lists SQL at the top of a CV anymore, but everybody still tests it.
If you want the whole thing on one page, I already made it. One sheet, every core concept. Buried in a thank you note from earlier this month 👇🏻
Every layer of your stack will be replaced. The data will still live in tables.
— Josep
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What's the SQL concept you still look up every single time? Reply and tell me 👇🏻