Google Professional Data Engineer Question 151
Select 2Google Cloud PlatformYou have been tasked with designing a data architecture for a retail analytics platform that processes large amounts of transaction and customer data in near real-time. The platform requires complex analytical queries and dashboards for business users, as well as efficient access to raw data for data scientists. Which combination of architectural components would best support these diverse data access patterns?
- A
Use BigQuery for analytical queries and dashboards, and Cloud Storage for storing raw data.
- B
Use Cloud SQL for analytical queries and dashboards, and export data periodically to Cloud Storage for data scientists.
- C
Use BigQuery for analytical queries and dashboards, and Dataproc with Cloud Storage for raw data processing and access.
- D
Use Firestore for transactional data storage and BigQuery for analytical queries and dashboards.
- E
Use Cloud Spanner for raw data storage and BigQuery for analytical queries and dashboards.
Show answer and explanation
Correct answers: A, C
Explanation
To support diverse data access patterns, it's essential to use tools that are purpose-built for specific needs. BigQuery is ideal for analytical queries and dashboards due to its scalability and SQL-like interface. For raw data storage, Cloud Storage is cost-effective and integrates well with processing frameworks like Dataproc, which provides flexibility for data scientists to process and analyze data. Together, these components form a robust architecture for handling both analytical and raw data access patterns.
- A. Correct.
This is correct because BigQuery is optimized for analytical queries and dashboards, while Cloud Storage is a cost-effective and scalable solution for storing raw data for data scientists.
- B. Incorrect.
This is incorrect because Cloud SQL is not designed to handle complex analytical queries at scale, and periodically exporting data to Cloud Storage adds unnecessary overhead.
- C. Correct.
This is correct because BigQuery can handle analytics and dashboards, while Dataproc combined with Cloud Storage provides a scalable and flexible environment for raw data processing and access, which is ideal for data scientists.
- D. Incorrect.
This is incorrect because Firestore is a NoSQL database optimized for transactional and document-based use cases, not for analytics or dashboards.
- E. Incorrect.
This is incorrect because Cloud Spanner is a globally distributed relational database designed for transactional workloads, not for raw data storage or analytics.