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 - Confluent

Kai Wähner works as Technology Evangelist at Confluent. Kai’s main area of expertise lies within the fields of Big Data Analytics, Machine Learning, Integration, Microservices, Internet of Things, Stream Processing and Blockchain. He is regular speaker at international conferences such as JavaOne, O’Reilly Software Architecture or ApacheCon, writes articles for professional journals, and shares his experiences with new technologies on his blog ( Contact and references: / @KaiWaehner /

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