You need to aggregate application events by disjoint hourly intervals from a Pub/Sub topic and load results into BigQuery at large scale. Which processing architecture should you use?

A streaming Dataflow job with tumbling windows is the most suitable architecture for continuous, large-scale aggregation of events into disjoint hourly intervals. Dataflow is designed for high-throughput, low-latency stream processing and automatically handles scaling and fault tolerance. Tumbling windows provide fixed, non-overlapping time intervals, perfect for hourly aggregations. Cloud Functions are not ideal for continuous, large-scale stream processing. Triggering a Cloud Function for each message would be inefficient and costly for high volumes. Scheduling a Cloud Function hourly to pull messages would introduce significant latency and complexity in managing state for aggregations across multiple pulls. A batch Dataflow job run hourly would also introduce latency and might miss events if the batch interval doesn't align perfectly with event arrival, and it's not designed for continuous processing.