Google Professional Cloud Developer Question 155
Select 2Google Cloud PlatformYou are building a real-time analytics application that processes high volumes of sensor data from IoT devices. The data must be ingested continuously, queried in real-time, and stored for long-term analysis. Which combination of Google Cloud storage options would best fit these requirements?
- A
Cloud Bigtable for real-time analytics and Cloud Storage for long-term archiving
- B
Cloud Firestore for real-time analytics and Cloud SQL for long-term storage
- C
Cloud Spanner for real-time analytics and Cloud BigQuery for long-term analysis
- D
Cloud BigQuery for real-time analytics and Cloud Storage for long-term archiving
- E
Cloud Bigtable for real-time analytics and Cloud BigQuery for long-term analysis
Show answer and explanation
Correct answers: A, E
Explanation
To handle high volumes of IoT sensor data, you need a real-time analytics solution like Cloud Bigtable, which is optimized for time-series and high-throughput workloads. For long-term analysis, Cloud BigQuery is the best choice because it is designed for querying large datasets efficiently, while Cloud Storage is suitable for archiving raw data if needed. The correct combinations are Cloud Bigtable with Cloud Storage or Cloud Bigtable with Cloud BigQuery, as they fulfill the ingestion, querying, and storage requirements effectively.
- A. Correct.
Cloud Bigtable is highly optimized for real-time, high-throughput use cases, while Cloud Storage is a cost-effective solution for long-term archiving of raw data.
- B. Incorrect.
Cloud Firestore is better suited for application data and transactional use cases, but it is not designed for high-throughput real-time analytics. Cloud SQL is not optimal for long-term storage of large datasets like IoT data.
- C. Incorrect.
Cloud Spanner is a distributed SQL database designed for transactional consistency and scalability, but it is not optimized for real-time analytics. Cloud BigQuery is a good choice for long-term analysis but not for real-time ingestion.
- D. Incorrect.
Cloud BigQuery is excellent for data analysis and querying but has higher latency, which makes it unsuitable for real-time analytics. Cloud Storage is suitable for long-term archiving but does not provide analytical capabilities.
- E. Correct.
Cloud Bigtable is ideal for real-time analytics due to its high throughput and low latency for large-scale time-series data, while Cloud BigQuery is optimized for long-term data analysis and querying, making this combination a perfect fit.