Google Cloud Digital LeaderInfrastructure modernization with Google CloudHard
An IoT company collects vast amounts of time-series data from millions of sensors globally. This data needs to be ingested at extremely high throughput, stored, and then queried for patterns and anomalies. The queries often involve filtering by timestamp and device ID, and aggregations over time windows. The company requires a database that can handle petabytes of data with high write and read throughput for analytical purposes. Which Google Cloud database service is best suited for this scenario?
- ACloud SQL
- BCloud Bigtable
- CCloud Spanner
- DFirestore
Show answer & explanationAnswer & explanation
Correct answer: B. Cloud Bigtable
Cloud Bigtable is a petabyte-scale, low-latency NoSQL wide-column database ideal for high-throughput operational analytics and time-series data. Its design excels at ingesting massive amounts of data and performing quick lookups and aggregations based on row keys (often composed of device ID and timestamp), making it perfect for IoT sensor data.
Why the other options are wrong
- A. Cloud SQL is a relational database not designed for petabyte-scale, high-throughput time-series data or wide-column analytical queries.
- C. Cloud Spanner is a globally distributed relational database for transactional workloads, not optimized for high-throughput time-series operational analytics.
- D. Firestore is a NoSQL document database for mobile/web apps, not suitable for petabyte-scale time-series data with high write/read analytical throughput.
Cloud Bigtable
A fully managed, NoSQL wide-column database service for large analytical and operational workloads, designed for petabyte-scale data and high throughput.
- Ideal for time-series data, IoT, and operational analytics.
- Offers extremely high read and write throughput.
- Scales horizontally to petabytes of data.
Memory trick: Bigtable: Big data, big speed, big analytics.