Google Professional Data Engineer Question 132
Single answerGoogle Cloud PlatformYou are working on a data analytics project that involves processing large volumes of transactional data. The data needs to be stored for long-term analysis, queried frequently for reporting, and must remain cost-efficient. Which storage solution and configuration should you choose to balance cost and performance effectively?
- A
Store the data in Cloud Spanner and use its query capabilities for analysis.
- B
Use Cloud Storage with Nearline storage class for cost savings and perform analysis by loading the data into BigQuery as needed.
- C
Store the data in BigQuery and leverage its built-in analytics capabilities for querying the data.
- D
Use Cloud Storage with Coldline storage class to minimize costs and perform analysis using Dataflow.
Show answer and explanation
Correct answer: C
Explanation
BigQuery is a serverless, fully-managed analytics platform designed for querying large datasets efficiently. It eliminates the need for manual data movement and provides cost-effective, high-performance analytics capabilities. In this scenario, frequent querying and long-term storage are key requirements, which make BigQuery the optimal choice.
- A. Incorrect.
Cloud Spanner is optimized for transactional workloads and high consistency, but it is not the most cost-efficient option for long-term storage and frequent querying of large datasets. This makes it suboptimal for the use case described.
- B. Incorrect.
Cloud Storage with Nearline storage class is cost-effective for data accessed less frequently than monthly, but loading data into BigQuery repeatedly for querying can increase costs and introduce latency, making it less ideal for frequent analysis.
- C. Correct.
BigQuery is purpose-built for large-scale analytics and querying. It provides an efficient, cost-effective solution for storing and analyzing large datasets with frequent queries. It is the best choice for this scenario.
- D. Incorrect.
Cloud Storage with Coldline storage class minimizes storage costs but is designed for infrequent access. Querying data stored in Coldline regularly would incur high retrieval fees, and using Dataflow for analysis adds additional costs and complexity.