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Introduction to Data Streaming

Originally aired:

About the Session

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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