Professional Data EngineerDesigning data processing systemsHard
A global ride-sharing company processes millions of sensor data points per second from vehicles, including GPS coordinates, accelerometer readings, and engine diagnostics. This data is critical for real-time fleet management, predictive maintenance, and driver safety analytics. The company requires a database that can handle extremely high write throughput, efficient time-series queries, and scale globally to petabytes of data with minimal latency. Which Google Cloud database is best suited for this demanding workload?
- ACloud Bigtable
- BFirestore
- CBigQuery
- DCloud SQL
Show answer & explanationAnswer & explanation
Correct answer: A. Cloud Bigtable
Cloud Bigtable is specifically designed for very large analytical and operational workloads that require high throughput and low latency, making it ideal for time-series data like sensor readings. Its wide-column NoSQL model excels at handling petabytes of data with consistent performance for both writes and time-series-based reads, perfectly fitting the requirements for real-time fleet management and predictive maintenance.
Why the other options are wrong
- B. Firestore is a document database suitable for web/mobile applications, but not built for the petabyte-scale, high-throughput time-series data processing described.
- C. BigQuery is an analytical data warehouse, excellent for complex queries over large datasets, but not designed for the extremely high-frequency, low-latency writes and operational reads of real-time time-series data.
- D. Cloud SQL is a relational database not optimized for the extreme write throughput and petabyte-scale time-series data access required.
Cloud Bigtable for Time-Series Data
Cloud Bigtable is a petabyte-scale, low-latency, wide-column NoSQL database optimized for large analytical and operational workloads, especially well-suited for time-series data, IoT, and high-throughput applications.
- Handles millions of writes/second.
- Consistent low-latency performance.
- Ideal for time-series, IoT, and operational analytics.
- Scales to petabytes of data.
Memory trick: Bigtable: The 'Big Clock' for your time-series data.