Trade volumes continue to grow, demanding more scalable solutions for real-time processing. The old approach was to throw hardware at the problem. However, that is no longer needed. Newer more efficient technologies enable smaller, more manageable clusters while handling extreme workloads. Hazelcast, the Open Source real-time data platform, can process billions of events per second with modest compute resources. Meanwhile, FINOS Perspective provides an ideal framework for real-time data visualization. In this session, you will learn how to architect and build the fastest data processing applications that scale linearly, and combine streaming trade data with reference and contextual data and supporting machine learning. Unifying data-in-motion and data-at-rest with real-time ML opens new use-cases across front and back office. We will take you through the end-to-end framework and an example Trade application, built on Hazelcast. We will also show how you can leverage SQL to further explore the operational data in the solution including querying Kafka topics and key-value data on the in-memory data store. Attendees will also get access to the Github sample application shown.