Blog

Today we announce a Private Preview of dltHub working with Databricks Unity Catalog Pages and Genie Ontology. Together, dltHub and Databricks can provide trusted answers on new data from day one.

We are releasing our new observability UI, the interface to the dltHub context graph, to General Availability. Find out how this will help you solve missing data when everything looks right at the surface from your pipelines.

We benchmarked dltHub, Fivetran, Airbyte, and Stitch on 191.5M rows from Postgres to BigQuery to measure their performance, ease of use and customizability.

Summary of the ​16:00 Panel discussion: The future of data teams and their tools hosted by MotherDuck

Teams connect Claude to HubSpot, Stripe and Zendesk and call it a stack. It’s good at questions that live in one system and touch a handful of records. Everything else breaks in ways hard to predict.

Openflow covers the short tail with connectors maintained by Snowflake. dltHub covers the long tail with connectors your agents generate and you own. Here is how they fit on one Snowflake account.

AI agents are changing data engineering faster than the profession can keep up. We looked at what the pioneers are doing, what still holds them back, and what it takes to turn AI capability into reliable production work.

dltHub background agents are in public preview, starting with job-inspector: a verified agent that diagnoses failed pipeline jobs with read-only access and posts the cause, the evidence, its confidence and a recommendation to fix to Slack.

A data platform should run like a power grid: routine faults cleared automatically inside a bounded scope, anything risky escalated to a person. This is the four-level autonomy ladder dltHub is climbing, and the context graph that makes each step safe to take.

In March 2024, we published “dlt adds Reverse ETL”. This piece walks through an example GTM workflow: turning a hiring signal into enriched, sales-ready company and people records, then reverse-ETL them to Attio.

In previous blogs we talked about how to build canonical models. This blog takes that further: we declare a BigQuery property graph over those models, query it with GQL, then ask it questions in plain English. The ontology resolved in the first step is what makes the answers right at the end.