BigQuery FinOps Query Pack

Rank the last 30 days of queries by bytes billed, with the on-demand dollar cost for each.

The ranking that matters if you are on Editions — slot-milliseconds consumed, not bytes scanned.

Find the queries that read every partition of a partitioned table — usually the single largest avoidable cost in a BigQuery estate.

Find the queries reading every column when they need a handful.

Find the repeated query shapes that are candidates for a materialized view or a scheduled table.

Jobs that consumed slot time or billed bytes and then errored out.

Cost by user and service account over the last 30 days, with a daily trend.

Break query spend down by job label — the only allocation dimension BigQuery gives you.

Daily cache hit rate plus the bytes that caching saved you.

The 30-day hourly profile you need before deciding anything about reservations. Aggregates only — no query text.

Per-hour slot consumption from JOBS_TIMELINE, which shows concurrency the job table cannot.

Reservation utilisation by hour — baseline paid for against slot-time consumed.

Queue time by job — the symptom of undersized capacity.

Tables ranked by storage, split into active and long-term, logical and physical.

The active/long-term split by dataset, which decides half your storage bill.

Storage you are paying for that nothing reads.

Partitioned tables that will grow forever because nothing ages out.

The invisible storage overhead behind frequently-rewritten tables.

Per-dataset comparison of both billing models at current list prices.

Service-level month-over-month movement from the billing export.

The SKU-level breakdown behind a service total.

Unallocatable spend by project — the denominator for any showback effort.

Credits broken out by type, which is how you verify a CUD is applying.

Daily cost against a trailing 7-day mean, flagged by standard deviations.