Today’s applications combine various forms of data processing and analysis to support multiple workloads.  Traditional batch processing and data warehousing combined with the more recent “real-time” stream processing satisfy an unprecedented breadth of analysis.  And now the way these applications respond to this analysis is presenting a new generation of Intelligent Applications.
There are many  potential and actual uses of application like this:

Fraud Detection
Credit Risk Management
Product Recommendations
Operational Efficiency

This session is going to examine how to successfully build this modern data architecture on the Microsoft Azure cloud platform.  Using a canonical IoT application example, we will focus on design tenants for efficient, scalable and resilient data processing and analysis while meeting the demands of the new Intelligent Application. By the end of the session we will have a solutions showing us how to

Allow only certain devices to send us data 
Store potentially high frequency sensor data 
Process the real-time data 
Enrich the data 

Microsoft Azure offers an array of services but the framework of our discussion will be the next generation of the lambda architecture:  How we add in machine learning and automated response to create the intelligent application architecture.