How does a Stream Analytics job operate on input data?

A Stream Analytics job operates primarily by running SQL queries to process real-time input data. This capability allows developers to analyze incoming data streams quickly and respond to events as they occur. Stream Analytics is specifically designed for processing large volumes of data in motion, and using SQL-like queries makes it accessible for those familiar with SQL, streamlining the process of defining complex event processing logic.

The job can aggregate, filter, and transform data in real time, delivering outputs to various destinations such as databases, event hubs, or dashboards. This capability is crucial for scenarios where timely insights are necessary, such as in monitoring sensor data from IoT devices, financial transactions, or social media feeds.

The other choices involve aspects that are not the primary operation of a Stream Analytics job. While encrypting data is essential for secure transmission, it is handled outside of the Stream Analytics processing itself. Machine learning algorithms could be used in conjunction with Stream Analytics but are not a core part of how the job operates with input data. Lastly, while data storage is important, Stream Analytics focuses on immediate data processing rather than long-term data storage for subsequent analysis.