Using Azure Stream Analytics for Real-time fraud detection

Azure Stream Analytics is a fully managed service providing low-latency, highly available, scalable complex event processing over streaming data in the cloud. For more information, see [Introduction to Azure Stream Analytics](http://azure.microsoft.com/en-us/documentation/articles/stream-analytics-introduction/). In this tutorial we will walk through the basics of setting up and using Stream Analytics to solve the problems described in the below Telecommunications and SIM fraud scenario. You will use a sample events generator to create and push events into an Azure Event Hub instance, setup and configure a new Stream Analytics job, write and test Stream Analytics queries for aggregation and alerting, and have the results sent to Azure Blob Storage to gain insight over data in real time. ###Scenario: Telecommunications and SIM fraud A telecommunications company has a large volume of data for incoming calls. They want to pare this data down to a manageable amount and obtain insights about customer usage over time and geographical regions. They are also very interested in detecting SIM fraud (multiple calls coming from the same identity around the same time but in geographically different locations) in real time so that they can easily respond by notifying customers or shutting down service. These are canonical Internet of Things (IoT) like scenarios where there is a ton of telemetry or sensor data being generated and customers want to aggregate them or alert over anomalies. ### Prerequisites for this demo * Create a new account or use an existing account to Sign-in to [Windows Azure](https://manage.windowsazure.com/) * * Download the [TelcoGenerator solution](https://github.com/Azure/azure-stream-analytics/tree/master/DataGenerators/TelcoGenerator) from the Azure Stream Analytics GitHub ### Getting Started * Follow the [step-by-step walkthrough](http://azure.microsoft.com/en-us/documentation/articles/stream-analytics-get-started/)