Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
Distributed systems and event-driven applications have developers increasingly embracing new technologies that allow businesses to process data in real-time and at scale. Due to the plethora of data sources, many developers now prefer to embed real-time data pipelines to process and transform data into each service and core business application. This modern approach requires a 3rd generation stream processing engine to analyze, distribute and act on events in volume and at extremely low latency.
Hazelcast Jet® is a 3rd generation stream processing engine. In this webinar you’ll find out how easy it is to deploy and get started. Join us to learn how to build batch and stream processing pipelines using the fluent, high-level Java API of Hazelcast Jet.
Using live coding, you’ll learn how to:
Marko Topolnik is a senior engineer in the Jet Core team. He has been with Hazelcast® since 2015, holds a PhD in computer science and has a six-figure score on Stack Overflow.