The following is a guest blog post by Dmitri Riz from the InfoStrat Tech Blog.
Microsoft Azure Logic Apps is a service that allows you to design and build scalable solutions for app integration, data integration, system integration, enterprise application integration (EAI), and business-to-business (B2B) communication, whether in the cloud, on premises, or both.
One of the features I wanted to explore in more detail is the support for real-time fraud detection for transactions originating from Dynamics 365 or, speaking more broadly, the ability to detect and flag irregularities in incoming data streams in real time.
The tool to support this is Azure Stream Analytics. It is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data that can be used to get insights, build reports or trigger alerts and actions.
Set up an Azure Event Hub receiving real-time Dynamics 365 transaction data and a Stream Analytics job scanning this Event Hub and using SQL-like queries to detect suspicious data.
Note that this approach uses deterministic SQL queries to discover fraud and other abnormalities in the data stream and relies on pre-created logic to discover fraudulent patterns in data. I will look at how to use the magic sauce of AI / machine learning to approach the same problem in the following post.
Azure Event Hub
The stream analytics job in this example will use data ingested into an Azure Event Hub. Event Hubs is a highly scalable data ingestion pipeline that integrates to many Azure services and is very helpful with many real-time processing solutions.
To set it up, you need to create an event hub namespace, and add a new event hub to it (see walkthrough here). The event hub will ingest our event data and will provide the input stream for the stream analytics job.
I have created a namespace dr-fd-post and a single event hub in this namespace called dr-fd-posthub.