How to Apply Big Data Analytics and Machine Learning to Real Time Processing


The world gets connected more and more every year due to Mobile, Cloud and Internet of Things. "Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop to find patterns, e.g. for predictive maintenance or cross-selling. But how to increase revenue or reduce risks in new transactions? "Fast Data" via stream processing is the solution to embed patterns into future actions in real-time. This session discusses how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be integrated into real-time event processing. A live demo concludes the session

Language: English

Level: Beginner

Waehner Kai

Technology Evangelist - TIBCO

Kai Wähner works as Technology Evangelist at TIBCO. His main area of expertise lies within the fields of Application Integration, Big Data, Analytics, SOA, BPM, Cloud Computing, Java EE and Enterprise Architecture Management. Kai is speaker at international IT conferences such as JavaOne, ApacheCon or OOP, writes articles for professional journals, and shares his experiences with new technologies on his blog ( Contact: Twitter: @KaiWaehner. Find more details and references (presentations, articles, blog posts) on his website

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