Apache Spark is a fast and general engine for large-scale data processing. In contrast to Hadoop's two-stage disk-based MapReduce paradigm, Spark's in-memory primitives provide performance up to 100 times faster for certain applications. 

The Spark software stack includes a core data-proccessing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. 

This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. 

Presented by Carol McDonald, MapR Technologies 

Carol is an instructor and curriculum developer at MapR. She has extensive experience as a software developer and architect, building complex mission-critical applications in the banking, health ins and telecom industries. Carol has experience working  with Java Enterprise technologies in many roles of the software development life cycle, including design, development, training and technology evangelism. Prior to MapR,  Carol worked on the Software development of a health information exchange for the 3 major health ins companies in the USA, a Loan Servicing Application for Toyota, a Telecom Network Management Application for HP, and a Messaging server for IBM. As a Java Technology Evangelist at Sun Microsystems, Carol traveled worldwide, speaking at Sun Tech Days, JUGs, and conferences. Carol holds a BS in Geology from Vanderbilt University, and an MS in Computer Science from the University of Tennessee-Knoxville.