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
Today we are introducing the dltHub AI Workbench: an infrastructure layer for dltHub that makes AI-generated dlt pipelines trustworthy enough to run and deploy in production.
Matthaus Krzykowski
Stop PII leaks before they hit your warehouse. By using dlt and Pydantic to enforce data contracts, you can sanitize or quarantine sensitive fields the moment they’re ingested.
Aman Gupta
In this blog post, I will describe the actual, hard real world barriers that make your LLM setup collapse, and propose principles for making your systems work.
Adrian Brudaru
Add data quality gates to Microsoft Fabric with dlt. Validate schemas, catch bad records, and mask PII before data reaches your lakehouse and downstream analytics.
Rakesh Gupta
Production traces are scattered across databases, log aggregators, and storage buckets, and most of them aren't clean (input, output) pairs you can hand to a training job. This walkthrough shows how to build a dlt pipeline that extracts traces from any source, transforms them into structured conversation formats, and lands them as versioned Parquet on Hugging Face, ready for Distil Labs to generate synthetic training data and deliver a specialist model that beats the LLM you're running today.
Alena Astrakhantseva +1
From raw data to production ML: load, transform, embed, and publish curated datasets with declarative pipelines powered by dltHub.
Elvis Kahoro +2
Single-gate validation fails to decouple row-level syntax from batch-level semantics. Evolve from WAP to the AWAP protocol with this simple dlt tutorial to stop pipeline corruption at the source.
Roshni Melwani
Trying to force an LLM to reconstruct the 'world' using only a semantic layer is like trying to turn cheese back into milk. The information required to understand the original system was stripped away during the modeling process.
Adrian Brudaru
For the more classic data engineering crowd, here’s an explainer of how unstructured AI memory works, though the lens of what we know from working with structured data.
Adrian Brudaru
By upgrading only the generative model, we achieved a 3x accuracy boost but hit a hard ceiling, proving that not only LLMs are needed for good retrieval.
Aashish Nair
Remus Molnar
I didn't vibe-build a product. I wrote a messy scaffold that runs a pipeline, grabs the schema, and forces an agent to build a star schema. It works shockingly well.
Adrian Brudaru
