Nicolas Frankel

Developer Advocate, Hazelcast

Nicolas Frankel is a Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with narrower interests like Software Quality, Build Processes and Rich Internet Applications. Currently working for Exoscale. Also double as a teacher in universities and higher education schools, a trainer and triples as a book author.

 

Talks on Wurreka:

A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Beside that, what about continuous deployment? There’s no tool that allows to deploy new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that e.g. Kubernetes.

And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API?

In that talk, I’ll demo exactly that using Hazelcast and Hazelcast Jet - but it’s possible to re-use the principles that will be shown using other streaming technologies.

Kotlin is one of those “new” JVM languages that are currently rocking the boat. Although it’s made a great impact on Android, it’s equally good on the server side. As Domain-Specific Languages are constrained by the language they run on, Kotlin frees developers from Java fluent builders to propose something better.

Using the Vaadin web framework as an example, I’ll demo how one could design its own DSL with Kotlin.

While software is eating the world, those who are able to manage the huge mass of data will emerge out on the top.

The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.

In this talk, Nicolas will define the context in which the batch processing model was born, reasons behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most important characteristics. Nicolas' talk will conclude by describing one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. He will go through all requirements and design and finally, using an OpenData endpoint and the Hazelcast platform, he will try to impress you with a demo of it.

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