We mix static analysis on pre-tagged workloads with a small, safe simulation inside the customer’s warehouse. It’s been surprisingly accurate for cost impact, and we avoid triggering any heavy runs.
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.
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.