How to Leverage Machine Learning (R, Hadoop, Spark, H2O) for Real Time Processing


Big Data is key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns, e.g. for predictive maintenance or cross-selling. However: How do you increase revenue or reduce risks in new transactions proactively? Stream processing is the solution to embed patterns into future actions in real-time. This session discusses and demos how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be build and integrated into real-time event processing frameworks. The session focuses on live demos

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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