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    Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools

    Posted By: Underaglassmoon
    Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools

    Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools
    Apress | Database Management | October 26, 2016 | ISBN-10: 1484221982 | 421 pages | pdf | 25.33 mb

    Authors: Vohra, Deepak
    This unique in-depth book that covers most of the commonly used Hadoop servicing frameworks or tools, not covered elsewhereThis book is practical with examplesHadoop and its ecosystem is one of the two most popular big data frameworks out there

    This book is a practical guide on using the Apache Hadoop projects including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout and Apache Solr. From setting up the environment to running sample applications each chapter is a practical tutorial on using a Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects MapReduce and HDFS and none discusses the other Apache Hadoop ecosystem projects and how these all work together as a cohesive big data development platform.

    What you'll learn
    How to set up environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5.
    How to run a MapReduce job
    How to store data with Apache Hive, Apache HBase
    How to index data in HDFS with Apache Solr
    How to develop a Kafka messaging system
    How to develop a Mahout User Recommender System
    How to stream Logs to HDFS with Apache Flume
    How to transfer data from MySQL database to Hive, HDFS and HBase with Sqoop
    How create a Hive table over Apache Solr

    Who this book is for:
    The primary audience is Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.

    Number of Pages
    XX, 421
    Number of Illustrations and Tables
    18 b/w illustrations, 293 illustrations in colour
    Topics
    Big Data
    Database Management



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