Sparkling Water integrates the H2O open source distributed machine learning platform with the capabilities of Apache Spark. It allows users to leverage H2O’s machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O’s Flow GUI which makes Sparkling Water a great enterprise solution. Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. These include the ability to use H2O frames as Apache Spark’s SQL datasource, transparent integration into Apache Spark machine learning pipelines, the power to use Apache Spark algorithms via the Flow GUI and easier deployment of Sparkling Water in a Python environment. In this talk we will introduce the basic architecture of Sparkling Water and provide an overview of the new features available in Sparkling Water 2.0. The talk will also include a live demo showing how to integrate H2O algorithms into Apache Spark pipelines – no terminal needed!
Jakub (or "Kuba") finished his bachelors degree in computer science at Charles University in Prague, and is currently finishing his master's in software engineering as well. As a bachelors thesis, Kuba wrote a small platform for distributed computing of tasks of any type. On his current masters studies he's developing a cluster monitoring tool for JVM based languages which should make debugging and reasoning about performance of distributed systems easier using a concept called distributed stack traces. At H2O, Kuba mostly works on our Sparkling Water project.