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A company is designing a new application that will process large volumes of streaming data from IoT devices. The solution requires low-latency ingestion and real-time analytics capabilities. Data will be stored for historical analysis for up to one year. Which Azure compute and data services should be recommended to meet these requirements?
- AAzure App Service, Azure Cosmos DB, Azure Blob Storage
- BAzure Virtual Machines, Azure SQL Database, Azure Data Factory
- CAzure Functions, Azure Database for PostgreSQL, Azure Synapse Analytics
- DAzure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage Gen2
Show answer & explanationAnswer & explanation
Correct answer: D. Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage Gen2
Azure Event Hubs is ideal for high-throughput, low-latency data ingestion from IoT devices. Azure Stream Analytics provides real-time processing and analytics of streaming data. Azure Data Lake Storage Gen2 is suitable for cost-effective, long-term storage of large volumes of data for historical analysis.
Why the other options are wrong
- A. Azure Cosmos DB is good for low-latency access but not primarily for high-volume streaming ingestion, and Azure App Service is for web applications.
- B. Azure Virtual Machines and Azure SQL Database are not optimized for real-time streaming data ingestion and analytics at scale.
- C. Azure Functions can process events but is not a dedicated streaming analytics platform, and Azure Synapse Analytics is for data warehousing, not real-time stream processing.
Azure Streaming Data Solution
A common Azure architecture for ingesting, processing, and storing high volumes of streaming data, typically from IoT or log sources.
- Event Hubs for ingestion
- Stream Analytics for real-time processing
- Data Lake Storage for long-term storage
Memory trick: Events flow like a stream, then get stored in a lake.