Recorded at SpringOne2GX 2015 Presenters: Ludwine Probst & Ilayaperumal Gopinathan Big Data Track Slides: http://www.slideshare.net/SpringCentral/apache-spark-for-big-data-processing

Today, we live in the world of Big Data. Hadoop and MapReduce are highly dominant in the domain of large scale data processing. However, the MapReduce model shows its limits for various types of treatment, especially for highly iterative algorithms frequently encountered in the field of Machine Learning.

Spark is an in-memory data processing framework that, unlike Hadoop, provides interactive and real-time analysis on large datasets. Furthermore, Spark has a more flexible programming model and gives better performance than Hadoop.

In this talk, we aim at giving a portrait of Spark and at browsing its ecosystem, in particular Spark Streaming and MLlib with a concrete example. We will also show how you can use Spark with Spring XD, allowing you to take advantage of the strengths in each platform.