Blogsby GlyphComposable canonicals: from tribal data knowledge to versioned artifact
In this article, I'm exploring an AI-native architecture that aims to solve the traditional central data team problem.
7 of 800 posts
Wed Jul 291 post
Wed Jul 221 post
dlt vs dltHub: the five ecosystem layers dltHub adds
dlt handles ingestion. dltHub adds the five ecosystem layers you'd otherwise build yourself: AI harness, context catalog, transformation, orchestration, managed infra.
Tue Jul 211 post
Moving off Airbyte
Moving off Airbyte: what the switch to dlt does to your ops load, your bill, and who can build pipelines. Real numbers on cost, reliability, and maintenance.
Wed Jul 151 post
Productionize Python ETL Scripts: Migrate to dlt & dltHub
Schema breaks, OOM deaths, duplicate loads, bus factor of one. What it takes to make hand-rolled pipelines production-grade, and what it costs to run them after.
Tue Jul 141 post
Migrating from Fivetran: how the move actually works
What does the move do to my bill and my renewal? And how to move?
Thu Jul 91 post
Consulting is becoming software
dltHub ships four Migration Blueprints: Python scripts, dlt, Fivetran, and Airbyte to dltHub. LLM-native tooling turns multi-month, senior-only migration projects into weeks of work at a fraction of the cost.
Wed Jul 81 post
We are launching two dltHub Blueprints for agent spend: Agent Cost & Usage to understand it, Agent Distillation to optimize it
dltHub launches two Blueprints for agent spend: Agent Cost & Usage to break down what each model, person, and customer costs, and Agent Distillation (with distil labs) to replace expensive agents with cheaper specialist models.