Social media and user generated content are causing an ever growing data deluge. The rate at which we produce data is growing steadily, thus creating larger and larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness, and relatedness to ongoing events. However, current (de-facto standard) solutions for big data analysis are not designed to deal with evolving streams. In this talk we introduces SAMOA (Scalable Advanced Massive Online Analysis), a platform for mining big data streams. SAMOA provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification and clustering, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. It is written in Java and is available at http://samoa-project.net under the Apache Software License version 2.0.