Infrastructure
Towards self-healing data pipelines: dltHub inspector in ❄️ Cortex
A Snowflake Cortex Agent for dltHub jobs
Posts related to my favorite lakehouse solutions: ❄️ Snowflake + 🦆 DuckDB and MotherDuck + 🐻❄️ Polars + 🧊 Iceberg
Infrastructure
A Snowflake Cortex Agent for dltHub jobs
Infrastructure
dltHub's AI harness writes the pipeline, Snowflake's CoCo takes over after data landed. Together they ✨
Lakehouse
Cortex Code's model default? Turns out it's just a CREATE AGENT statement away.
Lakehouse
200 users, zero curation, and a bootstrap grant I didn’t ask for.
Infrastructure
Nobody documents dozens of tables with hundreds of columns by hand. So an agent does it, INFORMATION_SCHEMA keeps it honest, and Teams pings when a source API adds a column
Lakehouse
Masking decides what you can see. Data movement policies decide what you can take.
Infrastructure
LinkedIn's Marketing API needs a browser to authorize. Scheduled pipelines don't have browsers. I store the refresh token in Snowflake.
Lakehouse
One hour, five manual inputs, and $0.65 of tokens... enough to replace a daily Fivetran sync with a tested dltHub job
Lakehouse
Three Iceberg GAs in one week advancing the old tradeoff: open format without babysitting a bucket.
Lakehouse
MotherDuck shipped agent-native Python pipelines. The obvious thing to run inside one is dlt.
Lakehouse
How to configure Cortex Agent models for all agents at once
Lakehouse
The paperclip is back. He works at Pro Juventute, lives in Snowflake, and shows up in both Teams and OpenCode.