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Answer: Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
Option A is a viable choice since Apache Kafka can handle message ingestion and Cloud Dataflow is adept at streaming analysis with auto-scaling capabilities. However, Option C is superior because Cloud Pub/Sub, as a managed messaging service, excels in message ingestion with automatic scaling and ordered message delivery within specified windows, fulfilling the requirement for at-least-once processing and order maintenance. Cloud Dataflow complements this by providing a fully-managed solution for both batch and streaming data processing, capable of real-time large volume data processing with automatic scaling. Options B and D are less suitable as Cloud Dataproc is more aligned with batch processing rather than streaming analysis.
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Design a data processing pipeline that automatically scales with increasing load, ensures at-least-once message processing, and maintains message order within 1-hour windows. Which combination of technologies would you choose?
A
Use Apache Kafka for message ingestion and Cloud Dataflow for streaming analysis.
B
Utilize Apache Kafka to ingest messages and Cloud Dataproc for streaming analysis.
C
Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
D
Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.