Google Professional Data Engineer Question 134
Single answerGoogle Cloud PlatformYour organization is building a data analytics pipeline on Google Cloud to process and store terabytes of data daily. The data must be queried frequently by analysts for reports and insights. However, the organization also wants to minimize storage costs while ensuring optimal query performance. Which storage solution should you choose to balance cost and performance in this scenario?
- A
Google BigQuery with flat-rate pricing
- B
Cloud Storage with Nearline storage class
- C
Cloud Spanner
- D
Google BigQuery with on-demand pricing
Show answer and explanation
Correct answer: D
Explanation
Google BigQuery is specifically designed for analytical workloads and supports querying large datasets with excellent performance. In this scenario, on-demand pricing is the most cost-effective option for variable or unpredictable query patterns since you only pay for the data processed per query, avoiding the higher costs associated with flat-rate pricing or storage solutions not designed for analytics.
- A. Incorrect.
BigQuery with flat-rate pricing is suitable for predictable workloads with high query demand, but it's often more expensive for organizations with inconsistent or low query volumes compared to on-demand pricing.
- B. Incorrect.
Cloud Storage with the Nearline storage class is cost-effective for data that is infrequently accessed. However, it is not optimized for high-frequency querying and analytics.
- C. Incorrect.
Cloud Spanner is a globally distributed relational database service, but it is not designed or cost-efficient for large-scale analytics workloads compared to BigQuery.
- D. Correct.
BigQuery with on-demand pricing is cost-efficient for scenarios with variable query demand, and it is optimized for analytical workloads, making it suitable for this use case.