Google Professional Data Engineer Question 139
Select 3Google Cloud PlatformYour company is planning to migrate its on-premises data warehouse to Google BigQuery. The data team has identified the following requirements:
- Handle petabytes of data with high scalability.
- Perform complex analytical queries with low latency.
- Minimize operational overhead while ensuring cost efficiency.
- Integrate seamlessly with existing Google Cloud services such as Dataflow and Cloud Storage. Which of the following considerations should be prioritized when planning this migration?
- A
Leverage BigQuery's serverless architecture to eliminate infrastructure management.
- B
Design an optimized schema with denormalized tables for better query performance.
- C
Set up a dedicated BigQuery cluster to manage the compute resources efficiently.
- D
Use partitioning and clustering to optimize query performance and control costs.
- E
Implement a real-time ETL pipeline using traditional on-premises data processing tools.
Show answer and explanation
Correct answers: A, B, D
Explanation
When planning a migration to BigQuery, it is essential to leverage its serverless, fully-managed nature to reduce operational overhead. Optimizing the schema and using techniques like partitioning and clustering ensure efficient query performance and cost management. These considerations align with the requirements to handle large data volumes, perform complex queries efficiently, and integrate with other Google Cloud services.
- A. Correct.
BigQuery's serverless architecture eliminates the need for infrastructure management, aligning with the requirement to minimize operational overhead.
- B. Correct.
Designing a denormalized schema can improve query performance, which is particularly important for handling complex analytical queries on large datasets.
- C. Incorrect.
BigQuery does not require setting up a dedicated cluster as it is fully managed and serverless. This option is irrelevant to the scenario.
- D. Correct.
Partitioning and clustering help optimize query performance and control costs, making them critical considerations when handling large datasets in BigQuery.
- E. Incorrect.
Using traditional on-premises ETL tools for real-time processing is not optimal for integration with Google Cloud services like Dataflow and Cloud Storage.