title: Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem (Addison-wesley Data & Analytics Series)
author: Douglas Eadline
description: <b>Douglas Eadline</b> began his career as a practitioner and a chronicler of the Linux cluster HPC revolution and now documents Big Data analytics. Starting with the first Beowulf Cluster how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering virtually all aspects of High Performance Computing (HPC). Prior to starting and editing the popular ClusterMonkey.net website in 2005, he served as editor-in-chief for ClusterWorld Magazine, and was senior HPC editor for <i>Linux Magazine</i>. Currently, he is a writer and consultant to the HPC/Data Analytics industry and leader of the Limulus Personal Cluster Project (limulus.basement-supercomputing.com). He authored <i>Hadoop Fundamentals LiveLessons, Second Edition</i> (2015), and <i>Apache Hadoop YARN LiveLessons</i> (2014), and is coauthor of <i>Apache Hadoop&trade; YARN</i> (2014), all from Addison-Wesley. <p style="margin:0px;">&nbsp;
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  Apache Spark 
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  About the Tutorial 
  Apache Spark is a lightning-fast cluster computing designed for fast computation. It was 
  built on top of Hadoop MapReduce and it extends the MapReduce model to efficiently use 
  more types of computations which includes Interactive Queries and Stream Processing. 
  This is a brief tutorial that explains the basics of Spark Core programming. 
  Audience 
  This tutorial has been prepared for professionals aspiring to learn the basics of Big Data 
  Analytics using Spark Framework and become a Spark Developer. In addition, it would 
  be useful for Analytics Professionals and ETL developers as well.  
  Prerequisite 
  Before you start proceeding with this tutorial, we assume that you have prior exposure 
  to Scala programming, database concepts, and any of the Linux operating system 
  flavors. 
  Copyright & Disclaimer  
  © Copyright 2015 by Tutorials Point