Explanation:
Correct Options: C. BigQuery and A. Cloud Pub/Sub
BigQuery is the optimal choice for batch data processing due to its serverless architecture, scalability, and ability to handle complex queries on large datasets efficiently. However, in scenarios where real-time data ingestion is also a requirement alongside batch processing, combining Cloud Pub/Sub for real-time messaging with BigQuery for batch processing (Option E) could be considered.
Why other options are not correct:
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In the context of designing a scalable and cost-effective data processing pipeline for a financial analytics application, which Google Cloud service would you recommend for efficiently handling large-scale batch data processing tasks? Consider the need for high throughput, scalability, and the ability to perform complex analytical queries on petabytes of data. Choose the best option from the following:
A
Cloud Pub/Sub, due to its real-time messaging capabilities that ensure immediate data processing.
B
Google Kubernetes Engine, as it provides a flexible environment for deploying custom batch processing applications.
C
BigQuery, because it offers a fully managed, serverless data warehouse solution optimized for batch processing and complex analytical queries on large datasets.
D
Cloud Firestore, given its NoSQL database features that support high-speed data access and retrieval for web and mobile applications.
E
Both A and C, combining real-time data ingestion with batch processing capabilities for a comprehensive solution.