Java 8 introduced the Stream API as a modern, functional, and very powerful tool for processing collections of data. One of the main benefits of the Stream API is that it hides the details of iteration over the underlying data set, allowing for parallel processing within a single JVM, using a fork/join framework.
Viktor will talk about a Stream API implementation that enables parallel processing across many machines and many JVMs.
You will learn how you can use the same API to process massive data sets across large clusters, which you already know how to do in a single JVM.
With an explanation of internals of the implementation, Viktor will give an introduction to the general design behind stream processing using DAG (directed acyclic graph) engines and how an actor-based implementation can provide in-memory performance while still leveraging industry-wide known frameworks as Java Streams API.
Viktor Gamov, Hazelcast
Viktor Gamov is a Senior Solution Architect at Hazelcast, the leading open-source in-memory data grid (IMDG). Viktor has comprehensive knowledge and expertise in enterprise application architecture leveraging open source technologies. He has helped leading organizations build low latency, scalable and highly available distributed systems. He is co-organizer of Princeton JUG and New York Hazelcast User Group. He is a co-author of O’Reilly's “Enterprise Web Development”. Viktor’s presenting at the conferences, blogging and producing a podcast. Follow Viktor on Twitter @gamussa.