For many years now, Hadoop, the open-source combination of Map-Reduce libraries and the Hadoop Distributed File System (HDFS), has been the start and end of any conversation involving processing of large volumes of data. That has changed. Last year, we revealed that the Hadoop community was reducing its focus on the Map-Reduce paradigm in favor of a more flexible distributed system management paradigm known as YARN. YARN can support several data processing frameworks running side by side as applications (e.g. Map-Reduce, Storm, Tez) as well as many other types of frameworks, including those that support infrastructure beyond the purview of big data (e.g. web servers). In addition, several new file formats have emerged and HDFS is starting to take a back seat to new file systems based on SSD and memory! In short, the Big Data world we now live in has expanded beyond the borders of Hadoop -- it now includes several Interactive-speed OLAP engines, multiple machine learning platforms, a couple of columnar file formats, and many alternatives for both streaming and graph processing. Increasingly, sentences that begin with Hadoop often end with Spark. How are companies leveraging these new Big Data technologies? Come to this track to learn more.