Provide granularity anomaly alerts of individual job or script execution

Providing granular anomaly alerts for individual BigQuery jobs, SQL queries, or script executions would be very useful because the current alerting model tells teams that BigQuery spend spiked, but not always which exact execution caused it, who ran it, or whether it should be stopped/reviewed. Scripts or queries that repeatedly executed against large datasets, and that an alert when a job exceeds something like $50 would reduce investigation time. Faster root cause analysis: Today, a service-level anomaly alert may say BigQuery spend increased, but the team still needs to investigate which job, query, script, user, dataset, or service account caused it. BigQuery audit logs can answer “who did what, where, and when,” while INFORMATION_SCHEMA views can provide job-level metadata such as jobs run during a time period, user, referenced tables, slot usage, and bytes processed. Prevents runaway query/script costs: A user may accidentally rerun a query multiple times against a large dataset, forget a filter, use a wildcard table, or run a Python notebook/script that repeatedly submits expensive BigQuery jobs. Google notes that BigQuery on-demand costs are driven by bytes processed, and custom quotas can help control processed data at the project or user level. Better accountability and education: If the alert includes the user email or service account, FinOps and application teams can follow up with the right owner. BigQuery metadata can be grouped or filtered by project, user, tables referenced, and other dimensions, which makes it suitable for detailed workload analysis.