Dealing with real-time, in-memory, streaming data is a unique challenge and with the advent of the smartphone and IoT (trillions of internet connected devices), we are witnessing an exponential growth in data at scale. Learning how to implement architectures that handle real-time streaming data, where data is flowing constantly, and combine it with analysis and instant search capabilities is key for developing robust and scalable services and applications.
In this lab session, we will look at how to implement an architecture like this, using reactive open source frameworks.
The streaming data architecture has the following tiers:
* Data collection tier
* Data transport tier
* Analysis tier
* In-Memory data store tier
* Data access tier
* Client tier
An architecture based around the Swiss rail transport system will be use throughout the lab.
Lab session technologies: Java (attendees must be comfortable with Java 8), Infinispan, Vert.x, OpenShift.