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What looked like an idiomatic BigQuery MERGE was scanning the full target table on every batch. The fix was syntactic, not architectural — and it was the single biggest contributor to a 57% data-warehouse cost reduction across the Tata Group engagement.
₹100 Cr / ~$12M in proven savings across a year-plus engagement. The four levers that did the heavy lifting, the lever I expected to win that didn't, and the post-engagement playbook that became a Searce managed service.
We built a small Go + Python service that parses a project's INFORMATION_SCHEMA, asks Gemini to classify each top-spending query against a catalog of anti-patterns, and recommends a rewrite. It is not a magic box; it is a pipeline that cuts the human review time per query from 20 minutes to 90 seconds.
Capacity-based slot reservation is the biggest single FinOps lever for predictable batch workloads, but the transition is harder than the math. Notes from sizing reservations across enterprise GCP customers.
Storage was the second-biggest line item on the Tata BigQuery bill. Long-term storage, physical-vs-logical billing, and column-level retention together took a 6-figure monthly line down to a 5-figure one.
BigQuery has had a built-in knowledge graph since 2024. For entity resolution across millions of rows — the "is this John Smith the same as that John Smith" problem — it's the cheapest tool I've found.