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!
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industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
Are you ready to make your machine learning algorithms operational within your business in real time?
In this webinar, we will walk through an architecture for taking a machine learning model from training into deployment for inference within an Open Source platform for real-time stream processing.
We will also cover:
John DesJardins is currently Community Advocate and CTO for North America at Hazelcast, where he is championing the growth of our Developer and Customer Community. His expertise in large scale computing spans Data Grids, Microservices, Cloud, Big Data, Internet of Things, and Machine Learning. He is an active blogger and speaker. John brings over 20 years of experience in architecting and implementing global scale computing solutions, including working with top Global 2000 companies while at Hazelcast, Cloudera, Software AG and webMethods. He holds a BS in Economics from George Mason University, where he first built predictive models, long before that was considered cool.