The correct option is Streaming.

Streaming is the right workload class because live telemetry requires continuous ingestion and low latency processing so events can be analyzed and acted on as they occur. Streaming supports windowing and incremental aggregation which lets the studio adjust game parameters and monitor events in near real time.

To build a streaming pipeline you typically pair a messaging ingress with a stream processing engine. For example you might ingest events with Cloud Pub/Sub and process them with a streaming engine such as Dataflow or another stream processor to compute metrics and trigger immediate actions.

Online transaction processing (OLTP) is wrong because OLTP describes transactional database workloads that focus on many small, ACID protected reads and writes rather than continuous event stream analysis. OLTP systems are not designed for the low latency event aggregation and real time analytics required for live telemetry.

Cloud Pub/Sub is wrong as the answer because it is a messaging service and not a class of workload. It can be part of a streaming solution by handling event ingress but the workload class that describes continuous, low latency processing is streaming.

Batch processing is wrong because batch jobs collect and process data in large groups at scheduled intervals which introduces latency. This makes batch unsuitable when you need immediate adjustments and real time monitoring of player telemetry.

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