Your company monitors millions of IoT sensors that emit timestamped readings every second. You need a fully managed service able to ingest this stream with petabyte-scale capacity, sustain sub-10 ms writes, and let applications quickly retrieve values by device ID and time. Which Google Cloud product best fits this workload?
Cloud Bigtable is purpose-built for high-throughput, low-latency workloads such as time-series and telemetry data. It scales horizontally to petabytes, supports millions of writes per second, and organizes data by a single row key (for example, device-ID-plus-timestamp), enabling efficient range lookups. BigQuery is optimized for analytical scanning, not millisecond-level point queries. Cloud SQL is a regional relational service that cannot scale to the required write throughput. Firestore is a document database suited to application data but is not designed for petabyte-scale ingestion with strict latency targets.
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What are the key features of Cloud Bigtable that make it suitable for IoT sensor data?
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How does Cloud Bigtable differ from BigQuery for time-series data use cases?
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Why is Firestore not suitable for petabyte-scale ingestion of IoT sensor data?
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What makes Cloud Bigtable suitable for time-series and telemetry data?
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What is the difference between Cloud Bigtable and BigQuery?
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How does Cloud Bigtable scale to handle petabyte-scale data?
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