Introducing Azure Stream Analytics
In the previous chapter, we discussed Azure Event Hubs, which is a solution for receiving and processing thousands of messages per second, by introducing the implementation of event-processor hosts. While it is great for workloads such as big data pipelines or Internet of Things (IoT) scenarios, it is not a solution to everything, especially if you want to avoid hosting virtual machines (VMs). Scaling such architectures can be cumbersome and nonintuitive—therefore, there is Azure Stream Analytics, which is an event-processing engine designed for high volumes of data. It fills a gap where other services such as Event Hubs or IoT Hub do not perform well (or where they do so, they require much more skill and/or more sophisticated architecture), particularly for real-time analytics, anomaly detection, and geospatial analytics. It is a great tool that will greatly improve your cloud- and message-processing skills.
