Google Professional Data Engineer Question 150
Select 2Google Cloud PlatformYour company is designing a data architecture for a new e-commerce platform. The platform needs to support two primary data access patterns: (1) real-time product recommendations based on recent user activity and (2) periodic analytical reporting on customer purchase trends. The solution must be cost-effective, scalable, and provide low-latency responses for real-time recommendations. Which combination of Google Cloud services would best meet these requirements?
- A
Use BigQuery for both real-time product recommendations and analytical reporting.
- B
Use Bigtable for real-time product recommendations and BigQuery for analytical reporting.
- C
Use Firestore for real-time product recommendations and Cloud SQL for analytical reporting.
- D
Use Pub/Sub and Dataflow for real-time product recommendations and BigQuery for analytical reporting.
- E
Use Memorystore for caching real-time product recommendations and BigQuery for analytical reporting.
Show answer and explanation
Correct answers: B, E
Explanation
Real-time product recommendations require a low-latency, scalable database such as Bigtable or an in-memory cache like Memorystore. BigQuery is highly suitable for periodic analytical reporting due to its ability to handle large-scale, complex queries efficiently. Using a combination of Bigtable or Memorystore for real-time needs and BigQuery for analytics ensures cost-effectiveness and scalability while meeting the required performance characteristics for both access patterns.
- A. Incorrect.
BigQuery is optimized for analytics and reporting but is not suitable for low-latency, real-time data requirements like product recommendations.
- B. Correct.
Bigtable is designed for high-throughput, low-latency workloads such as real-time product recommendations, while BigQuery is ideal for large-scale analytical reporting.
- C. Incorrect.
Firestore is suitable for document-based, application-specific data storage but does not provide the low-latency performance required for real-time recommendations. Cloud SQL is not ideal for large-scale analytical reporting.
- D. Incorrect.
While Pub/Sub and Dataflow can process real-time data streams, they are not designed for low-latency responses required for user-facing recommendations. BigQuery is appropriate for analytical reporting.
- E. Correct.
Memorystore, a managed in-memory data store, is well-suited for caching real-time recommendations to achieve low latency. BigQuery is excellent for analytical reporting.