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In this talk about Apache Flink we will touch on three main things, an introductory look at Flink, a look under the hood and a demo. * In the introduction we will briefly look at the history of Flink and then go on to the API and different use cases. Here we will also see how it can be deployed in practice and what some of the pitfalls in a cluster setting can be. * In the second section we will look at the streaming execution engine that lies at the heart of Flink. Here we will see what makes it tick and also what distinguishes it from other approaches, such as the mini-batch execution model. Ufuk Celebi - PMC member at Apache Flink and co-founder and software engineer at data Artisans * In the final section we will see a live demo of a fault-tolerant streaming job that performs analysis of the wikipedia edit-stream.

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Sean Owen – Director of Data Science @Cloudera Building machine learning models is all well and good, but how do they get productionized into a service? It's a long way from a Python script on a laptop, to a fault-tolerant system that learns continuously, serves thousands of queries per second, and scales to terabytes. The confederation of open source technologies we know as Hadoop now offers data scientists the raw materials from which to assemble an answer: the means to build models but also ingest data and serve queries, at scale. This short talk will introduce Oryx 2, a blueprint for building this type of service on Hadoop technologies. It will survey the problem and the standard technologies and ideas that Oryx 2 combines: Apache Spark, Kafka, HDFS, the lambda architecture, PMML, REST APIs. The talk will touch on a key use case for this architecture -- recommendation engines.

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