One working student, Claude Code, one stakeholder call, and 2 weeks. The migration worked but the workflow we used is the actual point of this post. AI alone wouldn’t have gotten us there.
Nikolas Jack Altran
Generally available today. 91% of new dlt pipelines are now built by agents. dltHub Pro makes building and running them production-grade for any Python developer.
Matthaus Krzykowski
Write your access policy as a plain-English ontology. Schema evolves; the LLM reads the rules and decides.
Aman Gupta
With 91% of dlt pipelines AI-written, learn Agentic Data Engineering in this free 1-hour course.
Adrian Brudaru
AI agents can write data pipelines. The part that isn't ready is everything around them — isolation, rollbacks, safe promotion to prod. This demo shows what a stack built for agents actually looks like.
Elvis Kahoro
Agents don't hallucinate. They navigate without a map. Ontology engineering is how you build one, and why every team pulling humans out of the loop needs it now.
Adrian Brudaru
The dltHub AI Workbench gives Claude Code a structured workflow for building data pipelines. We put it to the test with a real geopolitical question.
Roshni Melwani
dlt handles schema evolution efficiently but silently. Here's how to read dlt's metadata and be informed of what's shifting in your pipeline.
Aman Gupta
A "Success" exit code only tells you the pipeline ran. Use `load_id` to join `_dlt_loads` with your source table and check if the data is actually fresh.
Aman Gupta
We're in an LLM-coding junior bubble. "It runs" isn't the senior bar. Lifecycle rigor and dependency management are.
Adrian Brudaru
The dlt AI Workbench transforms AI-generated "vibe coding" from an unmanaged process full of hidden risks into a mature engineering workflow that prioritizes security, current documentation, and persistent state by default.
Adrian Brudaru
