Professional Data EngineerDesigning data processing systemsMedium
A global ride-sharing company processes millions of GPS coordinates per second from active vehicles. This data is critical for real-time tracking, dispatch, and estimated time of arrival (ETA) calculations, requiring extremely low-latency reads and writes (milliseconds). The data schema is simple: device ID, timestamp, latitude, longitude. Which Google Cloud database is best suited for storing and serving this high-velocity, low-latency operational time-series data?
- ACloud SQL
- BBigQuery
- CFirestore
- DCloud Bigtable
Show answer & explanationAnswer & explanation
Correct answer: D. Cloud Bigtable
Cloud Bigtable is a petabyte-scale, fully managed NoSQL database service specifically designed for large analytical and operational workloads, including time-series data. Its high throughput and low latency (single-digit milliseconds) make it ideal for ingesting and serving millions of GPS coordinates per second for real-time applications.
Why the other options are wrong
- A. Cloud SQL is a relational database not designed for the extreme scale and low-latency requirements of millions of time-series writes/reads per second.
- B. BigQuery is a data warehouse optimized for analytical queries on large datasets, not for high-throughput, low-latency operational point lookups or writes.
- C. Firestore is a NoSQL document database suitable for mobile/web applications, but its scale and latency characteristics are not optimized for millions of time-series writes per second like Bigtable.
Cloud Bigtable for Time-Series
A fully managed, wide-column NoSQL database optimized for high-throughput, low-latency reads and writes of large-scale time-series data.
- Petabyte-scale NoSQL database.
- Single-digit millisecond latency.
- Ideal for operational time-series (IoT, financial, geospatial).
- Suitable for high-velocity data ingestion.
Memory trick: Bigtable: Big data, big speed, for your time-series need!