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Answer: Use Azure Stream Analytics for real-time data processing, integrate it with Azure Data Factory using linked services, and design the pipeline to handle continuous data streams with appropriate triggers and activities.
Using Azure Stream Analytics for real-time data processing provides the necessary capabilities to handle continuous data streams from IoT devices. Integrating Stream Analytics with Azure Data Factory through linked services allows for seamless and efficient real-time data processing within the pipeline, meeting the dynamic requirements of IoT data feeds.
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Given a scenario where a data pipeline in Azure Data Factory needs to process real-time data feeds from IoT devices, describe how you would design the pipeline to handle this real-time data processing, including the use of Azure Stream Analytics and integration with Azure Data Factory.
A
Batch process the data at regular intervals without considering real-time requirements.
B
Use Azure Stream Analytics for real-time data processing, integrate it with Azure Data Factory using linked services, and design the pipeline to handle continuous data streams with appropriate triggers and activities.
C
Manually trigger the pipeline each time new data arrives from the IoT devices.
D
Store real-time data in a queue and process it in batches at a later time.
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