Question

A retail analytics team runs predictable, heavy BigQuery workloads every business day: several large scheduled ETL jobs plus dashboards that refresh throughout working hours. Their monthly on-demand bytes-scanned costs have become large and unpredictable, and finance wants a stable, capped monthly spend for the analytics workload. They also occasionally run one-off ad hoc exploration queries that should not block the production ETL jobs. Which approach best meets the requirement for predictable cost while isolating workloads?

Accepted Answer

Purchase BigQuery editions capacity with a baseline slot commitment, then create separate reservations and assign the ETL and dashboard projects to dedicated reservations to isolate them.

Answer

Keep on-demand pricing but add clustering and partitioning to every table so that bytes scanned drop below the monthly budget threshold.

Answer

Export all data to Cloud Storage and query it with BigQuery external tables to avoid slot charges entirely.

Answer

Move the workload to a single large Dataproc cluster running Spark SQL to obtain a fixed monthly compute cost.