Google Professional Cloud Developer Question 193
Select 2Google Cloud PlatformYour team is building a financial application that processes thousands of real-time transactions per second and requires low latency for immediate updates. However, you also need to run periodic analytical queries to generate business reports and trends. Which Google Cloud services should you use to meet both requirements effectively?
- A
Cloud Spanner for transactional processing and BigQuery for analytical queries
- B
BigQuery for both transactional processing and analytical queries
- C
Cloud SQL for transactional processing and BigQuery for analytical queries
- D
Firestore for transactional processing and Dataproc for analytical queries
- E
Pub/Sub for transactional processing and BigQuery for analytical queries
Show answer and explanation
Correct answers: A, C
Explanation
In this scenario, you need a solution for both OLTP (real-time transactional processing) and data warehousing (periodic analytical queries). Cloud Spanner or Cloud SQL are ideal for OLTP due to their low-latency, consistent, and scalable nature. BigQuery is the best choice for analytical queries due to its ability to handle large-scale datasets and complex queries efficiently. Combining Cloud Spanner or Cloud SQL for OLTP and BigQuery for analytics ensures the best performance and scalability for this use case.
- A. Correct.
Cloud Spanner is a globally distributed, strongly consistent database suitable for online transaction processing (OLTP), and BigQuery is a data warehouse optimized for analytical queries. This combination effectively satisfies both real-time transactions and analytics.
- B. Incorrect.
BigQuery is optimized for data warehousing and analytical queries but is not designed for online transaction processing (OLTP), which requires low-latency transactional support. Using BigQuery for both purposes would result in inefficiencies.
- C. Correct.
Cloud SQL is a managed relational database suitable for OLTP workloads, and BigQuery is ideal for data warehousing and analytical queries. This combination can also handle the scenario described effectively.
- D. Incorrect.
Firestore is a NoSQL database optimized for different use cases, such as document-based storage, and is not ideal for high-throughput OLTP. Dataproc is a managed Hadoop/Spark service for big data processing but is not designed for low-latency analytical queries.
- E. Incorrect.
Pub/Sub is a messaging service for event-driven architectures, not a database for OLTP. While BigQuery is a good choice for analytical queries, Pub/Sub cannot support transactional processing natively.