An online food delivery platform chooses to process incoming order events with Pub/Sub and Dataflow as they occur instead of loading log files into BigQuery once per hour. Which business benefit does this real-time streaming approach provide?
It automatically archives all raw events in a low-cost cold storage class, eliminating the need for Cloud Storage.
It removes the need to design a data schema before analysis, making all data modeling unnecessary.
It ensures processing jobs run only during off-peak hours, minimizing latency by avoiding resource contention.
It allows the platform to detect and react to operational issues or demand spikes within seconds, improving customer experience and revenue.
Streaming analytics lets an organization act on insights the moment data is generated. By ingesting events through Pub/Sub and processing them in Dataflow, the food-delivery company can immediately detect kitchen delays, driver shortages, or unexpected demand spikes and trigger in-app notifications or pricing changes while the situation is still unfolding. Batch processing would surface these patterns only after the hourly load completes, too late to influence the customer experience or protect revenue. The other statements describe capabilities-automatic archival, elimination of data modeling, and off-peak execution-that are either unrelated to streaming analytics or actually characteristic of batch jobs, so they are incorrect.
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