When we reach for it
Analytical workloads on Google Cloud, and anywhere ad-hoc querying over very large datasets has to be available without a cluster to run.
BigQuery engineering
Google’s serverless warehouse, with no infrastructure to manage and pricing driven by how much data a query touches.
When we would argue against it
Charging by data scanned makes an unpartitioned table an expensive mistake repeated on every query. Partitioning and clustering are not optimisations here, they are the design.
What it looks like in delivery
Partitioned and clustered from the first table, with cost per query visible to the people writing them — the fastest way to change how a team queries is to let them see what a query costs.
Where this appears on the site
Nothing on this site names it yet
We work in BigQuery, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Also in data and storage
Working in BigQuery?
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.