An IoT energy management company is designing a Google Cloud architecture to collect and process telemetry data from millions of smart electric meters. The architecture requires two storage capabilities: first, a scalable NoSQL database optimized for continuous high-throughput writes and low-latency reads of time-series device telemetry; second, block storage for worker Compute Engine virtual machines that provides durable storage surviving instance stops while delivering cost-effective performance. Which TWO Google Cloud storage solutions should be selected to meet these requirements? (Select TWO)
- Cloud Bigtable to handle the high-throughput time-series telemetry data ingestionCevap
- Balanced Persistent Disk (pd-balanced) attached to the Compute Engine worker instancesCevap
- CCloud SQL for PostgreSQL to store and query the massive real-time IoT time-series telemetry streams
- DLocal SSD attached to worker VMs to guarantee data durability across instance stops and maintain persistent VM state
Cevap
The correct architecture requires selecting Cloud Bigtable for high-throughput time-series telemetry ingestion and Balanced Persistent Disk (pd-balanced) for durable, cost-effective VM block storage.
Cloud Bigtable provides ideal high-throughput NoSQL storage for time-series IoT data streams, and Balanced Persistent Disk provides durable, cost-effective block storage that persists when Compute Engine virtual machines stop.
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Selecting GCP Database Engines and Block Storage Types