Databricks rebranded themselves as a "Data Intelligence Platform" at DAIS 2024, and they've been using the term consistently since. It's a more ambitious claim than "data lakehouse platform" — and it invites examination. What does "data intelligence" actually mean, and does
When you read about pgvector, you get the idealized version: add the extension, store vectors, query by cosine similarity, done. Production use adds the parts the tutorials leave out — index tuning, embedding model choice, the hybrid search problem, and the edge cases that only appear when your knowledge base has
Six months into working with UniForm and the broader interoperability story from DAIS 2024, and I want to document what's actually happening in production. The interoperability revolution that was announced at Summit is real — it's also slower and more complicated than conference demos suggest. What'
DAIS 2024 had a lot of sessions on training programs, certifications, and emerging job functions. The subtext underneath most of them was the same: the skills floor for data practitioners is rising, and it's rising fast. If you're not building that floor under yourself right now,
The noise around DeepSeek R1 was hard to separate from the signal for about 48 hours. Once the dust settled, the actual result was clear: a Chinese lab trained a reasoning model that matches OpenAI o1 on hard benchmarks, and they did it at a fraction of the reported cost.
The prototype worked. That meant it was time to stop prototyping and build the real thing. What had started as a PostgreSQL database with a JSONB blob and a pgvector column needed to become something I could rely on across multiple concurrent client projects, maintain without weekly intervention, and extend
The Data + AI Summit sessions on organizational readiness and AI culture had a consistent message: teams that are succeeding with AI aren't the ones with the most sophisticated technology. They're the ones that got the people, process, and platform working together. I've been saying
Every major conference has a set of announcements that get the keynote coverage and a set that get buried in session tracks and don't make the press release summary. At DAIS 2024, one announcement got almost no coverage relative to its long-term significance: the Unity Catalog lineage extension
A year in, it's worth being precise about what changed and what didn't. The AI-assisted development story in tech media tends toward two equally wrong poles: either these tools are transformative and everything is different now, or they're overhyped and nothing works as advertised.
It's December and I've had six months to see which DAIS 2024 announcements actually mattered and which were conference-stage noise. The scorecard is clearer now. Here's my honest read on what actually changed for the lakehouse in 2024. What Genuinely Moved the Industry Forward
Designing a system is more interesting than implementing one, which is probably why I spent two weeks in October drawing architecture diagrams before I wrote a single line of code for the new memory layer. The diagrams were useful. They forced decisions that are much harder to make once you
GM presented their data intelligence architecture at DAIS 2024, and it deserved more attention than it got. Most of the conference coverage focused on startup architectures and platform vendor announcements. GM is a 116-year-old manufacturer with hundreds of thousands of employees, decades of legacy systems, and data operations spanning manufacturing,