Blogsby GlyphOperational Health: Schema update detection with dlt
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.
8 of 608 posts
Fri Apr 242 posts
Geopolitical Market Dashboard with dlt & claude
We used the dltHub AI Workbench and Claude Code to build a live correlation dashboard of GDELT news, WTI crude, and Bitcoin in one afternoon.
Thu Apr 231 post
Operational Health: Auditing data freshness with dlt
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.
Tue Apr 211 post
Who maintains the skill layer?
We're in an LLM-coding junior bubble. "It runs" isn't the senior bar. Lifecycle rigor and dependency management are.
Tue Apr 143 posts
dltHub + Cortex Code: Two agents, better together
Cortex Code helps you work with data already in Snowflake. dltHub gets data into Snowflake from any source.
Why AI Agents Need a Guardrail Layer and What It Looks Like
How to stop AI agents from leaking credentials, skipping tests, and using outdated docs in data pipelines.
Agentic toolkit eval: dltHub REST API toolkit
Transforms AI-generated "vibe coding" from an unmanaged process full of hidden risks into a mature engineering workflow that prioritizes security.
Wed Apr 81 post
Minimum Viable Context for Building a Canonical Data Model
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.