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Answer: Use Azure Data Factory for batch processing and Azure Stream Analytics for real-time processing, with a shared data storage layer.
Option A is the correct approach as it leverages the strengths of both Azure Data Factory and Azure Stream Analytics for their respective processing types. By using a shared data storage layer, it allows for seamless integration and data flow between batch and real-time processing. Option B is not suitable as Azure Data Factory is primarily designed for batch processing, and Option C is not efficient as it requires separate data storage layers. Option D lacks integration between the two processing types, which may lead to data inconsistencies and increased complexity.
Author: LeetQuiz Editorial Team
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In a scenario where you need to implement a data pipeline that supports both batch and real-time processing, which of the following Azure services would you use and how would you architect the solution?
A
Use Azure Data Factory for batch processing and Azure Stream Analytics for real-time processing, with a shared data storage layer.
B
Use Azure Data Factory for both batch and real-time processing by configuring triggers and activities accordingly.
C
Use Azure Data Bricks for batch processing and Azure Functions for real-time processing, with separate data storage layers for each.
D
Use Azure Data Lake for batch processing and Azure Event Hubs for real-time processing, without any integration between the two.
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