What is the typical data flow in Azure Stream Analytics?

The typical data flow in Azure Stream Analytics involves several key stages that accurately depict how data is processed. The correct answer highlights that the flow starts with data input, continues with the application of a SQL-like query for processing, and concludes with the output being sent to various destinations.

This flow is essential as Azure Stream Analytics is designed for real-time analytics on streaming data. The input stage can consist of various streams of data from sources such as IoT devices, event hubs, or other real-time data producers. The core processing step involves writing SQL queries that define how to analyze the incoming data, applying various transformations and aggregations as needed.

Once the processing is complete, the results can be published to multiple outputs, such as Azure Blob storage, Azure SQL Database, or other services for visualization and further actions. This versatility makes Azure Stream Analytics a powerful tool for real-time data processing and decision-making scenarios in IoT and other applications.

Understanding this flow is critical for leveraging the full capabilities of Azure Stream Analytics, ensuring that data insights are timely and directed to the right places for analysis and action.