Name: [... and we're back!!!!] Real-Time Scalable Processing with Hazelcast and Kafka
Start: 2022-06-07T18:30:00+08:00
End: 2022-06-07T20:30:00+08:00
Location: PALO IT Singapore

We are glad to welcome you for our first in-person event since 2020!
Palo IT will be hosting us in their new space on Beach Road and we are glad to welcome **John DesJardins, CTO of Hazelcast**.

== Location
Palo IT, 11 Beach Road #06-01. Singapore 189675
Note: for those who are not able to come, it will be possible to attend our event online. We will post the link before the event.

== Schedule

* 6.30: Pizzas
* 7.00: Intro for the Java User Group team
* 7.15: Talk

== Speaker
John DesJardins is Chief Technology Officer at Hazelcast, setting technology strategy for the Real-time Intelligent Applications Platform. His expertise in large scale computing spans Big Data, Internet of Things, Machine Learning and Cloud. He is an active speaker and writer. John brings over 25 years of experience in architecting and implementing global scale computing solutions with top Global 2000 companies 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.

== Talk: Architecting Real-Time Scalable Processing with Java, Hazelcast and Kafka
Data volumes continue to grow, demanding new, more scalable solutions for low-latency data processing. Previously, the default approach to deploying such systems was to throw a ton of hardware at the problem. However, that is no longer sufficient. Meanwhile, newer technologies showcase a level of efficiency that enables smaller, more manageable clusters while handling extreme workloads. Processing billions of events per second on Kafka can now be done with a modest investment in compute resources with Open Source technologies like Hazelcast using simple Java APIs. In this session, you will learn how to architect and build ultra-fast data processing applications that scale linearly, and combine streaming data and reference data data-in-motion and data-at-rest with machine learning. We will take you through the end-to-end framework and example application, built on Hazelcast, an open-source software platform.

Michael Isvy

Jerome Bourgeon

Puneet Dewan

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