A biotechnology enterprise processes genomic sequencing data on Google Cloud across multiple research projects under a central Cloud Billing account. Raw sequencing files are ingested into Cloud Storage, and transformed analytical datasets are loaded into BigQuery for ad-hoc querying by data science teams. Recent audits show unpredictable BigQuery billing spikes caused by unconstrained, variable query workloads across teams. The organization requires a FinOps cost governance solution that establishes predictable budget boundaries without committing to rigid, unused baseline compute capacity. Which approach should the Cloud Architect recommend?
- APurchase 3-year static capacity commitments sized to peak query workloads for each project to achieve the highest possible commitment discount percentage.
- BMigrate the analytical querying workload from BigQuery to a multi-region Cloud Spanner instance to establish fixed hourly compute provisioning costs.
- Configure BigQuery Reservations using flexible autoscaling slot caps with Enterprise edition and assign reservations to project folders to control maximum expenditure while accommodating workload variance.Answer
- DMigrate all ad-hoc analytical query pipelines to Google Kubernetes Engine (GKE) running custom open-source query engines on Spot VMs.
Answer
Configure BigQuery Reservations using flexible autoscaling slot caps with Enterprise edition and assign reservations to project folders to control maximum expenditure while accommodating workload variance.
Configuring BigQuery Reservations with autoscaling slots and setting slot caps at the project folder level allows teams to handle fluctuating analytical query demands while enforcing hard budget guardrails to eliminate unexpected billing spikes.
Step-by-Step Solution
Key Concept
BigQuery Capacity Governance and Slot Reservations
Estimated Time:1m 30s