Mastering Hadoop - Couverture souple

Karanth, Sandeep

Couverture souple

ISBN 10 : 1783983647 ISBN 13 : 9781783983643

Editeur : Packt Publishing, 2014

Synopsis

Go beyond the basics and master the next generation of Hadoop data processing platforms

About This Book

• Learn how to optimize Hadoop MapReduce, Pig and Hive
• Dive into YARN and learn how it can integrate Storm with Hadoop
• Understand how Hadoop can be deployed on the cloud and gain insights into analytics with Hadoop

Who This Book Is For

Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.

What You Will Learn

• Understand the changes involved in the process in the move from Hadoop 1.0 to Hadoop 2.0
• Customize and optimize MapReduce jobs in Hadoop 2.0
• Explore Hadoop I/O and different data formats
• Dive into YARN and Storm and use YARN to integrate Storm with Hadoop
• Deploy Hadoop on Amazon Elastic MapReduce
• Discover HDFS replacements and learn about HDFS Federation
• Get to grips with Hadoop's main security aspects
• Utilize Mahout and RHadoop for Hadoop analytics

In Detail

Hadoop is synonymous with Big Data processing. Its simple programming model, "code once and deploy at any scale" paradigm, and an ever-growing ecosystem makes Hadoop an all-encompassing platform for programmers with different levels of expertise.

This book explores the industry guidelines to optimize MapReduce jobs and higher-level abstractions such as Pig and Hive in Hadoop 2.0. Then, it dives deep into Hadoop 2.0 specific features such as YARN and HDFS Federation.

This book is a step-by-step guide that focuses on advanced Hadoop concepts and aims to take your Hadoop knowledge and skill set to the next level. The data processing flow dictates the order of the concepts in each chapter, and each chapter is illustrated with code fragments or schematic diagrams.

A propos du livre

About This Book

• Learn how to optimize Hadoop MapReduce, Pig and Hive
• Dive into YARN and learn how it can integrate Storm with Hadoop
• Understand how Hadoop can be deployed on the cloud and gain insights into analytics with Hadoop

Who This Book Is For

Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.

What You Will Learn

• Understand the changes involved in the process in the move from Hadoop 1.0 to Hadoop 2.0
• Customize and optimize MapReduce jobs in Hadoop 2.0
• Explore Hadoop I/O and different data formats
• Dive into YARN and Storm and use YARN to integrate Storm with Hadoop
• Deploy Hadoop on Amazon Elastic MapReduce
• Discover HDFS replacements and learn about HDFS Federation
• Get to grips with Hadoop's main security aspects
• Utilize Mahout and RHadoop for Hadoop analytics

In Detail

Hadoop is synonymous with Big Data processing. Its simple programming model, "code once and deploy at any scale" paradigm, and an ever-growing ecosystem makes Hadoop an all-encompassing platform for programmers with different levels of expertise.

This book explores the industry guidelines to optimize MapReduce jobs and higher-level abstractions such as Pig and Hive in Hadoop 2.0. Then, it dives deep into Hadoop 2.0 specific features such as YARN and HDFS Federation.

This book is a step-by-step guide that focuses on advanced Hadoop concepts and aims to take your Hadoop knowledge and skill set to the next level. The data processing flow dictates the order of the concepts in each chapter, and each chapter is illustrated with code fragments or schematic diagrams.

Biographie de l'auteur

Sandeep Karanth

Sandeep Karanth is a technical architect who specializes in building and operationalizing software systems. He has more than 14 years of experience in the software industry, working on a gamut of products ranging from enterprise data applications to newer-generation mobile applications. He has primarily worked at Microsoft Corporation in Redmond, Microsoft Research in India, and is currently a cofounder at Scibler, architecting data intelligence products. Sandeep has special interest in data modeling and architecting data applications. In his area of interest, he has successfully built and deployed applications, catering to a variety of business use cases such as vulnerability detection from machine logs, churn analysis from subscription data, and sentiment analyses from chat logs. These applications were built using next generation big data technologies such as Hadoop, Spark, and Microsoft StreamInsight and deployed on cloud platforms such as Amazon AWS and Microsoft Azure. Sandeep is also experienced and interested in areas such as green computing and the emerging Internet of Things. He frequently trains professionals and gives talks on topics such as big data and cloud computing. Sandeep believes in inculcating skill-oriented and industry-related topics in the undergraduate engineering curriculum, and his talks are geared with this in mind. Sandeep has a Master's degree in Computer and Information Sciences from the University of Minnesota, Twin Cities. Sandeep's twitter handle is @karanths. His GitHub profile is https://github.com/Karanth, and he writes technical snippets at https://gist.github.com/Karanth.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 1783983647
• ISBN 13: 9781783983643
• Reliure: Broché
• Nombre de pages: 374
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible