Introduction
Today, massive amounts of real-time data are generated by connected applications, Internet of Things (IoT) devices and sensors, and various other sources. The proliferation of streaming data sources has made the ability to consume and make informed decisions from these data in near-real-time an operational necessity for many organizations.
Some typical examples of streaming data workloads include:
- Online stores analyzing real-time clickstream data to provide product recommendations to consumers as they browse the website.
- Manufacturing facilities using telemetry data from IoT sensors to remotely monitor high-value assets.
- Credit card transactions from point-of-sale systems being scrutinized in real-time to detect and prevent potentially fraudulent activities.
Azure Stream Analytics provides a cloud-based stream processing engine that you can use to filter, aggregate, and otherwise process a real-time stream of data from various sources. The results of this processing can then be used to trigger automated activity by a service or application, generate real-time visualizations, or integrate streaming data into an enterprise analytics solution.
In this module, you'll learn how to get started with Azure Stream Analytics, and use it to process a stream of event data.