GCP Professional Cloud Architect Practice Question

A utility company is ingesting 20 million smart-meter readings per minute through a Cloud Dataflow streaming job. Each reading (deviceId, timestamp, kWh) must be stored with single-digit-millisecond latency and later retrieved by deviceId and time-range for real-time billing dashboards. The data set is expected to grow to 10 PB over the next two years, and multi-row ACID transactions are not required. Which Google Cloud storage service best satisfies these operational requirements while minimizing manual sharding effort?

Cloud Bigtable
Cloud Spanner
BigQuery
Cloud Firestore

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Answer Description

Cloud Bigtable is designed for very high write and read throughput that scales linearly by adding nodes, and it delivers single-digit-millisecond latency for key-value lookups and range scans. It automatically handles petabyte-scale datasets without manual sharding and is ideal for time-series data such as IoT or meter readings. Cloud Spanner provides strong transactional semantics but has higher per-row overhead and is optimized for relational workloads. BigQuery excels at large analytical queries but cannot serve low-latency per-row operations. Firestore supports document workloads but imposes per-collection throughput limits and is not intended for multi-petabyte time-series streams.

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What is Cloud Bigtable and how does it handle large datasets efficiently?

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What are the key differences between Cloud Bigtable and Cloud Spanner for IoT workloads?

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Why is Cloud Bigtable ideal for time-series data?

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How does Cloud Bigtable handle scalability for petabyte-scale datasets?

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What makes Cloud Spanner unsuitable for this use case?

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GCP Professional Cloud Architect

Designing and planning a cloud solution architecture

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