A company wants to analyze IoT sensor data from millions of devices in near real-time and store the raw data for batch processing. Which combination of Azure services should they use?
Ingests and stores raw data efficiently.
Why this answer
Azure Event Hubs is a highly scalable data streaming platform and event ingestion service capable of ingesting millions of events per second from IoT devices in near real-time. Azure Blob Storage provides cost-effective, durable object storage for the raw data, which can then be used for batch processing with services like Azure Data Lake Analytics or Azure Synapse. This combination directly meets the requirements for near real-time ingestion and raw data storage for batch processing.
Exam trap
The trap here is that candidates often confuse Azure IoT Hub with Azure Event Hubs, assuming IoT Hub is required for all IoT scenarios, but Event Hubs is the correct choice for high-throughput, near real-time event ingestion without device management overhead.
How to eliminate wrong answers
Option A is wrong because Azure Synapse Analytics is primarily a data warehouse for structured analytics, not optimized for storing raw, unstructured IoT data for batch processing; using it for raw storage would be costly and unnecessary. Option B is wrong because Azure IoT Hub is a device management and messaging service, not a high-throughput event ingestion pipeline for near real-time analytics, and Azure Cosmos DB is a NoSQL database for transactional workloads, not designed for storing massive volumes of raw data for batch processing. Option C is wrong because Azure Data Lake Storage is a storage service, not an ingestion service; it cannot ingest and buffer streaming data in near real-time, and Azure Stream Analytics is a real-time processing engine, not a storage solution for raw data.