A client’s data-platform bill had grown into one of their largest operating costs — and nobody could say which workloads earned it. A line-by-line audit found the answer: the waste wasn’t in contracts or idle servers. It was in how the queries ran.
The platform processed large volumes daily on a major cloud data warehouse. Costs had climbed steadily with data growth, and the finance question — ‘is this what it should cost?’ — had no owner and no answer. Console recommendations had been applied; the bill kept rising, because the expensive patterns lived in query design and data layout, where consoles don’t look.
The client kept the savings — because the fixes addressed causes (data layout and query patterns) rather than pausing symptoms, and because guardrails now flag new waste within a billing cycle. A side effect nobody minded: several of the most expensive reports also got faster, since waste and slowness were the same underlying design issues.
Every major cost mapped to the workload and query pattern behind it — the bill translated into engineering terms for the first time.
The heaviest queries profiled: scan volumes, slot usage, retry behavior. A small number of patterns accounted for most of the spend.
Partitioning and clustering redesigns, query rewrites, and materialization of repeated heavy computations — sequenced by savings per unit of risk.
Budgets, anomaly alerts, and a monthly cost review format, so the next waste gets caught while it’s small.
Thirty minutes. Bring the problem as it actually is — the stalled pilot, the bill, the spreadsheet everyone quietly maintains. We’ll tell you honestly whether we’re the right builder for it.
You leave with a straight answer on fit — and if it fits, a free Blueprint to follow.