TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?

The correct answer is to have the vehicle's computer compress the data in hourly snapshots and store it in a GCS Coldline bucket. This approach reduces costs by performing compression at the source, minimizing data transfer and storage size. GCS Coldline is an excellent choice for infrequently accessed data like historical telemetry for future machine learning model training, offering lower storage costs than Nearline. Storing hourly snapshots is a practical balance between data freshness and cost-efficiency for this use case. Storing in a GCS Nearline bucket would be more expensive than Coldline for data accessed less than once a month. Pushing data in real-time to Dataflow and then BigQuery or Bigtable would incur higher processing and storage costs, as real-time streaming is not required for data that will be used for future, non-real-time machine learning model training. BigQuery is optimized for analytics, and Bigtable for high-throughput, low-latency access, neither of which is the primary requirement here for cost-effective raw data storage for future ML.