Everyone's buzzing about these AI code review tools. Claims about catching bugs, improving quality. Sounds familiar. Remember when every vendor promised "intelligent" data lineage?
Before I waste my team's time, I need a real pilot. Not a vendor demo on their curated perfect repo.
Question is scope. Roll it out to the entire data engineering team of 12? Or pick one gnarly, active project first?
My instinct says single project. Less noise, easier to measure signal. But management loves "broad adoption" metrics.
If you've been through this, what actually worked?
- Did team-wide adoption just drown everyone in false positives?
- Did a single project pilot give you enough evidence to decide?
Concrete example: we run dbt + BigQuery. PRs are mostly SQL models and YAML. If the tool can't handle a 500-line `SELECT` with three CTEs, it's useless.
SQL is enough