Today's stacks split ingestion, transformation, orchestration, and the context that agents need gets lost at every boundary. dltHub Transformations runs ingestion, transformation, lineage, and verification inside the same execution context, so an LLM can reason about your business with the context a senior analyst would have.
Adrian Brudaru
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
