2 min readfrom Data Science

LLMs for data pipelines without losing control (API → DuckDB in ~10 mins)

Our take

Are you ready to transform your approach to data pipelines? Join us on February 17 for a live session where we’ll explore a practical workflow that combines LLMs with DuckDB, allowing you to maintain control while streamlining data ingestion. We’ll start from a scaffolded template and navigate real challenges like complex APIs and nesting issues, all while validating results through metadata. Bring your toughest API challenges and discover how AI can support your engineering process without losing oversight. Let’s redefine data management together!
LLMs for data pipelines without losing control (API → DuckDB in ~10 mins)
LLMs for data pipelines without losing control (API → DuckDB in ~10 mins)

Hey folks,

I’ve been doing data engineering long enough to believe that “real” pipelines meant writing every parser by hand, dealing with pagination myself, and debugging nested JSON until it finally stopped exploding.

I’ve also been pretty skeptical of the “just prompt it” approach.

Lately though, I’ve been experimenting with a workflow that feels less like hype and more like controlled engineering, instead of starting with a blank pipeline.py, I:

  • start from a scaffold (template already wired for pagination, config patterns, etc.)
  • feed the LLM structured docs
  • run it, let it fail
  • paste the error back
  • fix in one tight loop
  • validate using metadata (so I’m checking what actually loaded)

LLM does the mechanical work, I stay in charge of structure + validation

AI-assisted data ingestion

We’re doing a live session on Feb 17 to test this in real time, going from empty folder → github commits dashboard (duckdb + dlt + marimo) and walking through the full loop live

if you’ve got an annoying API (weird pagination, nested structures, bad docs), bring it, that’s more interesting than the happy path.

we wrote up the full workflow with examples here

Curious, what’s the dealbreaker for you using LLMs in pipelines?

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