With 91% of dlt pipelines AI-written, learn Agentic Data Engineering in this free 1-hour course.
Adrian Brudaru
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
Part of the [dltHub AI Workbench series](https://dlthub.com/blog/ai-workbench)
Adrian Brudaru
TL;DR: Cortex Code helps you work with data already in Snowflake. dltHub Pro gets data into Snowflake from any source, especially the ones no ETL tool covers. They operate at different layers of the stack and they are designed to hand off to each other.
Adrian Brudaru
Call it the MVC problem: minimum viable context. Too little and it hallucinates your domain. Too much and it drifts from your actual goal. The process has to be controlled.
Hiba Jamal
How are LLMs supposed to know the business logic of how you use Hubspot, Luma and Slack together? How are they supposed to know what a customer means to you?
Hiba Jamal
