Monitor BigQuery queries in near real time to identify the most costly queries and which users incur cost. Which approach provides this?

The correct approach is to create a Cloud Logging sink exporting BigQuery data access logs to BigQuery, then query the resulting table. BigQuery data access logs contain detailed information about each query, including the user who ran it and the resources consumed (e.g., bytes processed). Exporting these logs to BigQuery allows you to perform complex SQL queries to identify costly queries and attribute them to specific users in near real-time. Labeling datasets per user and filtering billing reports is insufficient because billing reports provide aggregated cost data, not detailed query-level metrics. Exporting logs to Cloud Storage and using Dataflow is overly complex and introduces unnecessary latency compared to a direct BigQuery export. Enabling billing export to BigQuery provides cost data, but it's not as granular as BigQuery data access logs for identifying specific costly queries and their resource consumption.