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Answer: Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
The question emphasizes storing telemetry data for machine learning training next year while minimizing costs. Option D (compressing data in hourly snapshots and storing in GCS Coldline) is optimal because Coldline storage is designed for infrequent access (data retrieval after 90+ days), aligning with the 'next year' requirement. It offers the lowest storage cost ($0.007/GB/month) compared to Nearline ($0.01/GB/month) or BigQuery/Bigtable. Compression reduces storage needs, and GCS integrates well with ML workflows. Community discussion (73% support for D, with upvoted comments highlighting cost-effectiveness) reinforces this. Option A (Nearline) is less suitable due to higher storage costs for infrequent access. Options B and C involve real-time streaming with Dataflow, which incurs higher compute costs and is unnecessary for delayed ML use.
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TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. They plan to use this data next year to train machine learning models. They want to store this data in the cloud while minimizing costs.
What should they do?
A
Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
B
Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
C
Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
D
Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket