Google Professional Data Engineer Question 120
Single answerGoogle Cloud PlatformYour organization collects large volumes of IoT sensor data from multiple edge devices deployed globally. The data must be stored in a way that supports low-latency access for real-time analytics and allows for automatic scaling as the volume grows. Additionally, the system should be able to handle semi-structured data and provide strong integration with other Google Cloud services for downstream analysis. Which storage solution should you choose?
- A
Cloud Bigtable
- B
Cloud SQL
- C
Cloud Spanner
- D
Cloud Storage
Show answer and explanation
Correct answer: A
Explanation
Cloud Bigtable is the best fit for the scenario as it is purpose-built for handling large volumes of semi-structured data with low-latency access, making it ideal for IoT sensor data and real-time analytics. It also integrates seamlessly with other Google Cloud services like Dataflow and BigQuery for downstream analysis.
- A. Correct.
Cloud Bigtable is a highly scalable NoSQL database designed for low-latency access and is suitable for storing time-series and semi-structured data, making it the best choice for IoT sensor data and real-time analytics.
- B. Incorrect.
Cloud SQL is a managed relational database service, but it is not optimized for handling large-scale, semi-structured IoT data or near real-time analytics.
- C. Incorrect.
Cloud Spanner is a globally distributed relational database and is better suited for transactional workloads rather than low-latency access and semi-structured data storage.
- D. Incorrect.
Cloud Storage is an object storage service that is ideal for storing unstructured data like files and backups, but it is not optimized for low-latency real-time analytics.