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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

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

We discussed in the last two chapters about big data, Hadoop, and the Hadoop ecosystem. Now, let's discuss more technical aspects about Hadoop Architecture. Hadoop Architecture is extremely flexible, scalable, and fault tolerant. The key to the success of Hadoop is its architecture that allows the data to be loaded as it is and stored in a distributed way, which has no data loss and no preprocessing is required.

We know that Hadoop is distributed computing and a parallel processing environment. Hadoop architecture can be divided in two parts: storage and processing. Storage in Hadoop is handled by Hadoop Distributed File System (HDFS), and processing is handled by MapReduce, as shown in the following image:

Pillars of Hadoop – HDFS, MapReduce, and YARN

In this chapter, we will cover the basics of HDFS concept, Architecture, some key features, how Read and Write process happens, and some examples. MapReduce is the heart of Hadoop, and we will cover the Architecture...

The rest of the chapter is locked

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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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