You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub, processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

A Is not correct because the tumbling window function is not ideal for real-time processing as it creates fixed, non-overlapping windows which can lead to outdated information in a real-time dashboards B is correct because the hopping window function is suitable for the given scenario to aggregate for the past 30 seconds for every 2 seconds period. Storing the aggregate data in Cloud Memorystore ensures low latency access to the data which is essential for real time dashboards. C Is not correct because the session window function is not suitable for this scenario as it is meant to group data based on periods of activity. D is not correct because BigQuery is not meant for low latency access.