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A large manufacturing company wants to implement predictive maintenance for its industrial machinery. They have a massive volume of time-series sensor data (e.g., temperature, vibration, pressure) generated at high velocity from thousands of machines. This data needs to be ingested and processed in real-time to detect anomalies and predict failures. Which Google Cloud database is best suited for storing and analyzing this type of data?
- ACloud Bigtable
- BCloud SQL
- CCloud Spanner
- DFirestore
Show answer & explanationAnswer & explanation
Correct answer: A. Cloud Bigtable
Cloud Bigtable is a wide-column NoSQL database designed for large analytical and operational workloads, especially suitable for high-throughput, low-latency time-series data like sensor readings and IoT data. It excels at handling massive volumes of data with consistent performance.
Why the other options are wrong
- B. Cloud SQL is a relational database more suited for transactional data, not high-volume, high-velocity time-series data.
- C. Cloud Spanner is a globally distributed relational database, overkill and less cost-effective for pure time-series sensor data without strong relational transaction needs.
- D. Firestore is a document database for web/mobile apps, not optimized for petabyte-scale time-series analytics.
Cloud Bigtable
Cloud Bigtable is a fully managed, scalable NoSQL wide-column database service for large analytical and operational workloads, particularly well-suited for time-series data, IoT data, and financial data.
- High-throughput, low-latency performance for petabyte-scale data.
- Ideal for time-series data, IoT, and operational analytics.
- Consistent sub-10ms latency for reads and writes.
Memory trick: Bigtable handles 'Big' data, especially time-series, like a 'Table' of sensor readings.