Principles and Patterns for Streaming Data Analysis
|Big Data & Machine Learning|
Data is overwhelming us both in terms of size and speed. How do we deal with these huge amount of real-time, streaming data? We need to combine tools, platform, patterns, and principles to overcome this situation.
Join on us this session where we’ll identify critical patterns and principles that enable us to achieve greater scale and responde speed. We’ll provide you with a live demo demonstrating how an In-Memory Data Grid like Infinispan and a platform like Kubernetes can leverage these patterns and principles creating a state-of-the-art distributed data processing architecture.
Galder is one of the founding engineers of Infinispan, Red Hat's distributed in-memory data grid store. He is responsible for the client/server architecture and has recently been implementing a Node.js client. A seasoned conference presenter, Galder is on a mission to promote Infinispan wherever he goes. He's always happy to learn new technologies and programming languages to apply in live coding demos. He's particularly keen on functional programming related technologies, having used Scala since 2009 and Haskell more recently.