Google Cloud Digital LeaderData and AI with Google CloudHard
A logistics company needs to track millions of packages globally in real-time. Each package generates frequent updates (location, status, sensor data) that need to be ingested at high throughput and low latency, and then made available for immediate querying by internal dashboards and customer-facing applications. Which Google Cloud database is best suited for this high-volume, low-latency, wide-column NoSQL data?
- ACloud Bigtable
- BCloud SQL
- CCloud Spanner
- DFirestore
Show answer & explanationAnswer & explanation
Correct answer: A. Cloud Bigtable
Cloud Bigtable is Google Cloud's fully managed NoSQL wide-column database service, specifically designed for large analytical and operational workloads requiring high throughput and low latency for petabytes of data, making it ideal for IoT, time-series, and operational analytics like real-time package tracking.
Why the other options are wrong
- B. Cloud SQL is a relational database (SQL) and is not optimized for the extreme high throughput and low latency requirements of wide-column NoSQL data at this scale.
- C. Cloud Spanner is a globally distributed, strongly consistent relational database, but it's not a wide-column NoSQL database optimized for the specific access patterns and scale of sensor/time-series data like Bigtable.
- D. Firestore is a NoSQL document database, suitable for mobile/web apps, but Bigtable is better for petabyte-scale, high-throughput time-series or wide-column data.
Cloud Bigtable
A fully managed, scalable NoSQL wide-column database service designed for large analytical and operational workloads. It excels at high throughput and low latency for massive datasets.
- NoSQL wide-column database
- High throughput, low latency
- Petabyte-scale data
- Ideal for time-series, IoT, operational analytics
Memory trick: Bigtable handles 'BIG' data with 'BIG' speed for real-time tracking.