, Dynamicum [Ground Floor]
Is it still worth learning SQL in 2026, or can we just "chat" with our data? This hands-on tutorial explores that exact question by pushing Text-to-SQL to its absolute limits. This won't be just happy paths; we will deliberately expose where LLMs fail : ambiguity, hallucinations, and "dirty" data...and build the engineering stack required to fix them!
You will build a local data Agent from scratch using DuckDB, MCP and a minimalist semantic layer. By the end, you will understand the hard boundaries of AI reasoning, how a semantic layer acts as a safety net, and why knowing SQL is still (since 1974) the most critical skill for building reliable analytics agents.
This session is a "reality check" for AI analytics. We combine theory with engineering to answer one question: Where are the limits of Text-to-SQL? Participants will experience the frustration of a hallucinating LLMs and the satisfaction of fixing it with a realistic minimalist local setup.
Learning objectives:
1. Map the limits: Identify exactly where LLMs break (e.g., complex joins, specific business logic, non-standard schemas).
2. Bridge the gap: Learn how a semantic layer translates fuzzy English into deterministic SQL.
3. Modern architecture: Overview and hands-on on DuckDB Model Context Protocol (MCP) to give agents standard, safe tools to do analytics.
4. The verdict: Understand why SQL is becoming the "Assembly Language" of the AI era, and why you still need to be fluent in it and what is still missing to just "chat with our data".
Prerequisites:
- Laptop with Python 3.10+.
- Beginner SQL knowledge (joins, aggregations).
- No prior AI/LLM experience required.
I started my career in data 10+ years ago as a data engineer, working in large corporates like AXA setting up on-prem Spark clusters (yes, that old!) to tech unicorns building data platforms in the cloud at Klarna, Back Market, and Trade Republic.
Over the years, I found a passion for sharing what I learned and teaching others. It became my full-time job when I joined as the first DevRel at MotherDuck (DuckDB in the cloud) in 2023.
I believe learning should be fun. I enjoy making complex topics more approachable through storytelling and creativity.
I want to keep teaching curious students (in-person and online) and help the next generation learn not just data, but software engineering in this post-AI world.
I spent over 10 years as a consultant setting up data pipelines, data models, and cloud infrastructure for clients ranging from government to fintech to retail and energy, before joining MotherDuck to help people and their AI agents make the most of the platform through documentation, examples, and other content.
I am the co-author of The Fundamentals of Analytics Engineering, and I love writing about all things data — both at MotherDuck and on my personal blog at dumky.net.