Overview

Azure Stream Analytics is an event-processing engine that allows you to examine high volumes of data streaming from devices.
Incoming data can be from devices, sensors, web sites, social media feeds, applications, and more.
It also supports extracting information from data streams, identifying patterns, and relationships.
You can then use these patterns to trigger other actions downstream, like alerts, feed information to a reporting tool, or store it for later use.

Applications

IoT Sensor fusion and real-time analytics on device telemetry.
Web logs/click stream analytics
Geo-spatial analytics for fleet management and driver-less vehicles.
Remote monitoring and predictive maintenance of hi-value assets
Real-time analytics on Point of Sale data for inventory control and anomaly detection

Azure Stream Analytics starts with a source of streaming data that is ingested into Azure Event Hub, Azure IoT Hub or from a data store like Azure Blob Storage.
To examine the streams, you create a Stream Analytics job that specifies the input source that streams data.
The job also specifies a transformation query that defines how to look for data, patterns, or relationships.
The transformation query leverages a SQL-like query language that is used to filter, sort, aggregate, and join streaming data over a period of time.
When executing the job, you can adjust the event ordering options, and duration of time windows when performing aggregation operations.
After analyzing the incoming data, you specify an output for the transformed data and decide what to do in response. E.g. you can take actions like: