Concurrency without Migraines: Distributed Compute in Hazelcast

ABSTRACT
As businesses grow, many applications will run into scaling issues — what works fine at low transaction volumes doesn’t work well, or at all, at high volumes.

In-Memory technology solutions like the open-source Hazelcast Platform have helped many businesses overcome their scaling issues to provide high throughput and low latency at a tremendous scale. But even among long-time users of the platform, there is a tendency to focus on the in-memory storage (or caching) aspects of the solution.

In this talk, we’ll focus on how in-memory compute APIs help leverage the processing capabilities of a distributed cluster, so you aren’t leaving significant potential performance gains on the table. The combination of in-memory storage and in-memory compute provides a unique synergy that enables applications to address real-time use cases at any scale.

SPEAKER BIO
Mike Yawn is a Senior Solutions Architect with Hazelcast, where his focus is on pre-sales consulting for the Hazelcast Platform. Mike has over 30 years of industry experience in R&D and Consulting roles with some of the world's leading tech companies, including HP, Oracle, EMC, and eBay. Before joining Hazelcast, Mike developed, ported, and maintained Java-based technologies, including JVMs, just-in-time compilers, and debuggers. He was the founder and co-chair of SIG JAVA for Interex, the HP International User's group, during the early days of Java (JDK 1.0 to 1.3)