Google Professional Data Engineer Question 138
Select 4Google Cloud PlatformYou are designing a data warehouse solution on Google Cloud for a retail company that needs to process large volumes of transactional data and provide near real-time analytics to its analysts. Which considerations should you prioritize when planning the data warehouse architecture?
- A
Choosing a data warehouse solution that supports scalable storage and compute separation
- B
Ensuring the data warehouse integrates seamlessly with streaming data ingestion tools like Pub/Sub and Dataflow
- C
Optimizing the schema design to support transactional processing with frequent updates
- D
Evaluating query performance for complex analytical queries across large datasets
- E
Selecting a solution that provides built-in machine learning capabilities for predictive analytics
Show answer and explanation
Correct answers: A, B, D, E
Explanation
When planning a data warehouse on Google Cloud, it is important to prioritize considerations that align with the specific requirements of analytical workloads. These include scalability, integration with streaming data sources for near real-time analytics, and the performance of complex queries. Additionally, modern data warehouses like BigQuery offer built-in machine learning capabilities, which can add significant value to the overall architecture. However, transactional processing is better suited for OLTP databases rather than data warehouses.
- A. Correct.
Scalable storage and compute separation is essential for handling large volumes of data efficiently and providing flexibility in scaling resources according to workload demands.
- B. Correct.
Seamless integration with streaming data ingestion tools like Pub/Sub and Dataflow ensures that near real-time data processing and analytics requirements are met.
- C. Incorrect.
Transactional processing with frequent updates is not a primary focus for data warehouses, which are optimized for analytical workloads rather than OLTP (Online Transaction Processing).
- D. Correct.
Query performance for complex analytical queries is a critical consideration for data warehouses, as they are designed for OLAP (Online Analytical Processing) workloads over large datasets.
- E. Correct.
Built-in machine learning capabilities can enhance the value of the data warehouse by enabling predictive analytics directly within the platform.