A manufacturing company is designing an IoT solution to monitor equipment in real-time. Thousands of sensors send telemetry data every second. The data must be ingested, processed, and stored for analysis. The solution must handle high throughput and provide low-latency analytics. Additionally, the company wants to use Azure Machine Learning to predict equipment failures based on historical data. You need to design a data pipeline that meets these requirements. What should you include in the design?
IoT Hub, Stream Analytics, and Blob Storage form a scalable real-time pipeline.
Why this answer
Option A is correct because Azure IoT Hub ingests sensor data, Azure Stream Analytics processes the stream in real-time with low latency, and Azure Blob Storage stores the data for historical analysis. Option B is wrong because Azure Data Lake Storage is for big data analytics, not real-time processing. Option C is wrong because Azure Cosmos DB is a NoSQL database, but the primary processing should be with Stream Analytics.
Option D is wrong because Azure Event Hubs is for ingestion, but Azure Databricks is overkill for simple real-time analytics.