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!
Business today is being driven by two major technologies, the need to manage vast amounts of data streaming into IT systems continuously, as well as the need to manage this streaming data in the cloud. The operationalization of streaming data in a cloud environment is not necessarily difficult, but it can be complicated, and understanding the nuances is critical to your company’s long term success.
Streaming applications operate on continuous data, they are by design long-running. Bringing several new challenges:
This brings several new challenges:
Cloud environments (public, private or hybrid) provide resources that are transient, so the environment keeps changing adding another level of complexity.
In this webinar, we will present the tools that Hazelcast Jet brings to the table when it comes to operating long-running streaming applications in the cloud.
Vladimir is a product manager with an engineering background and deep expertise in stream processing and real-time data pipelines. Ten years of building internal software platforms and development infrastructure have made him passionate about new technologies and finding ways to simplify data processing. Vladimir co-authored two white papers on the topic: Understanding Stream Processing: Fast Processing of Infinite and Big Data, and A Reference Guide to Stream Processing. His tutorial video on stream processing and real-time data pipelines discusses the building blocks of a stream processing pipeline and demonstrates how developers can write a full-blown streaming pipeline in less than a hundred lines of Java code for a variety of applications. Vladimir is also a lecturer with the Czechitas Foundation, whose mission is to inspire women and girls to explore the world of information technology. Czechitas Foundation teaches coding in various programming languages, software testing, and data analysis.
Can is one of the founding members of the Hazelcast Jet team and is currently the engineering team lead. Prior to joining Hazelcast, he worked as a software development consultant to some of the world’s leading investment banks. He has deep interest in distributed systems, stream processing and building high-throughput, low-latency data pipelines. He is also a polyglot programmer with expertise in Java, Python, C# and functional programming.