These are ideas about dbt, what it actually changes, why teams keep running into the same problems, and what adoption really looks like.
Each one explores common framings on dbt and breaks down misunderstandings from my perspective.
Posts
Data Modeling
The real issue isn't the stored procedure. It's that the whole thing was built to run — not to be looked inside. That matters a lot more than syntax.
Architecture
For most of dbt's history, this was a straightforward question. AI changed the calculus — not because of AI features in dbt Platform, but because of what AI agents actually need from your data infrastructure.
Interactive walkthroughs
The same ideas, built as visual step-through experiences. Useful if you prefer seeing a concept before reading about it.
I work in data and spend a lot of time thinking about why good tools don't always get used. The gap between "this works" and "this gets used" is almost always a visibility or trust problem, not a capability one.