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Hadoop Essentials

You're reading from Hadoop Essentials Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem

Product type Paperback

Published in Apr 2015

Publisher Packt

ISBN-13 9781784396688

Length 194 pages

Edition 1st Edition

Languages

Java

Tools

Hadoop

Concepts

Data Processing

Author (1):

Shiva Achari

View More author details

Table of Contents (9) Chapters

Preface

1. Introduction to Big Data and Hadoop FREE CHAPTER

2. Hadoop Ecosystem

3. Pillars of Hadoop – HDFS, MapReduce, and YARN

4. Data Access Components – Hive and Pig

5. Storage Component – HBase

6. Data Ingestion in Hadoop – Sqoop and Flume

7. Streaming and Real-time Analysis – Storm and Spark

Index

Distributed programming

To leverage the power of a distributed storage filesystem, Hadoop performs distributed programming which can do massive parallel programming. Distributed programming is the heart of any big data system, so it is extremely critical. The following are the different frameworks that can be used for distributed programming:

MapReduce
Hive
Pig
Spark

The basic layer in Hadoop for distributed programming is MapReduce. Let's introduce MapReduce:

Hadoop MapReduce: MapReduce is the heart of the Hadoop system distributed programming. MapReduce is a framework model designed as parallel processing on a distributed environment. Hadoop MapReduce was inspired by Google MapReduce whitepaper. Hadoop MapReduce is scalable and massively parallel processing framework, which can work on huge data and is designed to run, even in commodity hardware. Before Hadoop 2.x, MapReduce was the only processing framework that could be performed, and then some utility extended and created a wrapper to program...

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Authors (1)

Achari

Achari

Shiva Achari has over 8 years of extensive industry experience and is currently working as a Big Data Architect consultant with companies such as Oracle and Teradata. Over the years, he has architected, designed, and developed multiple innovative and high-performance large-scale solutions, such as distributed systems, data centers, big data management tools, SaaS cloud applications, Internet applications, and Data Analytics solutions. He is also experienced in designing big data and analytics applications, such as ingestion, cleansing, transformation, correlation of different sources, data mining, and user experience in Hadoop, Cassandra, Solr, Storm, R, and Tableau. He specializes in developing solutions for the big data domain and possesses sound hands-on experience on projects migrating to the Hadoop world, new developments, product consulting, and POC. He also has hands-on expertise in technologies such as Hadoop, Yarn, Sqoop, Hive, Pig, Flume, Solr, Lucene, Elasticsearch, Zookeeper, Storm, Redis, Cassandra, HBase, MongoDB, Talend, R, Mahout, Tableau, Java, and J2EE. He has been involved in reviewing Mastering Hadoop, Packt Publishing. Shiva has expertise in requirement analysis, estimations, technology evaluation, and system architecture along with domain experience in telecoms, Internet applications, document management, healthcare, and media. Currently, he is supporting presales activities such as writing technical proposals (RFP), providing technical consultation to customers, and managing deliveries of big data practice groups in Teradata.

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