The dltHub REST API updated yesterday
No OpenAPI endpoint. So I unzipped the client library and diffed it.
No OpenAPI endpoint. So I unzipped the client library and diffed it.
Thousands of contacts without salutation, and a model that has to guess from a first name alone.
A Snowflake Cortex Agent for dltHub jobs
dltHub's AI harness writes the pipeline, Snowflake's CoCo takes over after data landed. Together they ✨
Hand-written patterns decided which Salesforce errors were noise. Now a classifier does it, and it says how sure it is.
Cortex Code's model default? Turns out it's just a CREATE AGENT statement away.
200 users, zero curation, and a bootstrap grant I didn’t ask for.
A watchdog to keep all dlt pipelines in line
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
Masking decides what you can see. Data movement policies decide what you can take.
dltHub ships Slack and email alerts out of the box. My org runs on Teams, so I built the third one myself and wired it into all ~70 pipelines with one decorator shadow.
Re-using shared patterns in a dlt workspace
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
Infrastructure
A public contact list sounds easy... until 2,110 websites, four languages, changing staff, and source evidence get involved. 😅
Infrastructure
Microsoft Planner has no native backup export, so I built a declarative dlt job that writes complete Graph snapshots to Azure Blob. 😎
Digital Analytics
I gave an agent a dependency graph to make it write notes on my GTM containers
Infrastructure
Every opencode session leaves a trail in a local SQLite file. dlt turns it into analytics you actually read.
Infrastructure
Exit code 137 is dlt's way of saying "your RAM and I need to talk"
Visualization
Same migration, opposite bets: rebuild as Streamlit code you own, or as Sigma workbooks behind a parity gate.
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.
Infrastructure
Bring your own model, ditch the subscription.
Lakehouse
How to configure Cortex Agent models for all agents at once