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Answer: Link an IoT gateway to Cloud Pub/Sub and leverage Cloud Dataflow for message processing.
The optimal solution involves connecting an IoT gateway to Cloud Pub/Sub and utilizing Cloud Dataflow for processing messages (Option C). This approach efficiently manages the sporadic spikes in message volume by leveraging Cloud Pub/Sub's scalability for incoming loads and Cloud Dataflow's managed processing capabilities. Option A, which suggests virtualizing a Kafka cluster and using Cloud Load Balancing, may not adequately address scalability and cost issues. Option B, involving Edge TPUs for data storage and transmission, is less scalable for long-term needs. Option D, integrating Cloud Dataflow with the Kafka cluster, might not resolve the underlying issue of volume spikes, as the Kafka cluster could still struggle with high message volumes.
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As the manager of a system that processes messages from IoT devices worldwide, you're facing challenges due to unpredictable spikes in message volume, especially from batched messages, making the current single on-premises Kafka cluster in the us-east region difficult and costly to manage. What Google Cloud architecture would best address this issue?
A
Virtualize a Kafka cluster on Compute Engine in us-east and utilize Cloud Load Balancing for global IoT device connectivity.
B
Employ Edge TPUs as sensor devices for the storage and transmission of messages.
C
Link an IoT gateway to Cloud Pub/Sub and leverage Cloud Dataflow for message processing.
D
Integrate Cloud Dataflow with the existing Kafka cluster to enhance message processing scalability.
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