Show HN: Zingle – an AI code reviewer for data teams (SQL/dbt/Airflow/Spark)

This is cool. We had a similar issue with dbt exploding refresh costs. Curious how you estimate warehouse cost on a PR? Static analysis or do you simulate?

I don’t love tools that block PRs automatically. Can this run in advisory mode only?

Hey HN - Zingle came out of a pretty painful reality we kept seeing across data teams. We were reviewing ~60 dbt/SQL PRs a week for a client, and the senior engineers were overloaded while analysts weren’t allowed to merge anything risky. The combination of fast-moving code and slow reviews led to mistakes. The worst one on our side was a PR that triggered repeated full refreshes on a big model and blew up into a $50k Snowflake bill. That’s when we realized we needed a reviewer that understands data behavior, not just code.

How Zingle works (technical outline): - SQL parser → identifies patterns, predicates, merge logic, join risks - Lineage graph engine → traces downstream models + dashboards - Warehouse metadata fetcher → table sizes, clustering, stats, partitions - Cost estimation engine → predicts warehouse impact (bytes scanned, compute, I/O) - Try re-creating affected downstream systems → safely runs new logic to analyse data diffs - Rules engine → custom governance checks (merge key, tests, docs, ownership)

We don’t store SQL, data, metadata, or logs. Nothing leaves the customer’s warehouse.

Would love any feedback - especially edge cases that are tricky or places where our reviewer’s judgment feels wrong or incomplete. Happy to answer any technical questions.

Congrats! Curious why the name Zingle tho? Sounds like a dating app haha.

Do you guys support Snowpark or just SQL right now?

Is this open source or closed source? Any plans for an on-prem version?