I wrote a deep dive for Data Engineer Things on how to build a semantic layer that moves beyond "SQL generation" to one that AI can actually reason with.

You can read the full article here: blog.dataengineerthings.org/semantic-lay...

4/4

This shifts the definition of a Semantic Layer:

❌ From: "Here is the math to calculate Churn." (SQL Generation)

✅ To: "Here is what Churn means, why we track it, and who owns it." (Reasoning)

We need to go from defining metric logic for BI to building knowledge graphs for AI. 3/4

But the reality has shifted: We are moving from Analytics (humans looking at charts) to Agents (AI taking actions).

Humans have implicit context ("ignore test accounts," "ask the VP"), while AI is context-blind.

If you give an Agent raw SQL snippets, it will hallucinate. 2/4

On a broader note, treating skills like writing concise emails or running effective meetings as things you just "pick up" is a massive institutional blind spot for universities & companies, and it costs us all daily. 2/2

In the course, I introduce a practical framework (+ tools & principles) to help you design scalable data models and ship impactful data products that deliver business value (and not just vanity metrics 🤷🏼‍♂️). 2/2
Watch it here:
www.moderndata101.com/masterclass/...

Data teams have a reputation for building cool things that aren't useful. I break down a simple two-step path to fix this: 1) Find low-hanging fruit for quick wins. 2) Dive deep into the business to find real problems, like enriching product analytics with raw event data. 4/4

Spotify Wrapped is great, but why is it such a rare example of a personal data product? We have mountains of data siloed in our apps. I explore how AI could be the "last-mile" enabler to connect these APIs and create a coherent narrative from the data of our own lives. 3/4

Data modeling is cool again, and that's good, but we need to adapt it to today's world. My new article proposes a "Go Wide, then Go Deep" strategy to adapt modeling for a world of data products. More in the newsletter. 2/4

In my latest article, I present a set of strategies, techniques, and frameworks for adapting data modeling to the world of data products - from distributed ownership to metric trees and entity-centric modeling. 2/2

blog.det.life/data-modelin...

This means data teams of all sizes can finally start building those experiences, making their data platforms more intuitive and powerful. AI is democratizing this capability, and the "last mile" is looking a lot more accessible. I dive into this topic in my latest blog post: shorturl.at/f9mDi
5/5

That simple experiment genuinely shifted my perspective. Building experience layers (the UIs and streamlined workflows on top of data components) isn't just for companies with massive engineering resources anymore. AI is truly acting as a "last-mile enabler" here. 4/5

Last weekend, using Replit's AI assistant, I tried building a basic dbt run timeline visualizer. The result? A functional (albeit basic) UI in under an hour. Just me, no big platform team. (Even Claude isn't a big fan of D3.js though 😅)
3/5

Historically, building this layer was a luxury few beyond Big Tech could afford - mainly because data teams are constantly overwhelmed and in firefighting mode. But AI is changing this, fast.
2/5

Constraint-heavy environments (niche tech, missing tools) force a different engineering discipline (+ recent talk by Jane Street as an example): Less abstraction reliance demands more creativity & deeper understanding. Can be frustrating, but valuable learning ground. 4/4