A ride-sharing platform ingests millions of GPS data points every second from vehicles around the world. The company needs a fully managed service that can store this high-volume, time-series data, deliver single-digit millisecond latency for the most recent location lookups, and automatically scale to petabytes without manual sharding. Which Google Cloud data product best meets these requirements?
Cloud Bigtable is designed for very large, high-throughput workloads such as IoT and time-series streams. Its distributed architecture supports millions of writes per second and provides single-digit millisecond latency for point reads and writes, while automatically scaling to petabytes of data. BigQuery is excellent for analytical queries but is optimized for batch ingestion rather than low-latency operational lookups. Cloud SQL offers familiar relational capabilities but cannot scale horizontally to the required write rate. Firestore is a serverless NoSQL document store suited to mobile back-ends, yet it cannot match Bigtable's sustained throughput at a global scale.
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What is Cloud Bigtable and how does it differ from other database services?
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How does Cloud Bigtable achieve single-digit millisecond latency?
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How does Cloud Bigtable automatically scale to petabytes of data?
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