Coverart for item
The Resource Hadoop mapreduce v2 cookbook : explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets, Thilina Gunarathne ; cover image by Jarek Blaminsky ; commissioning editor Edward Gordon ; copy editors Puja Lalwani, Alfida Paiva, Laxmi Subramanian

Hadoop mapreduce v2 cookbook : explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets, Thilina Gunarathne ; cover image by Jarek Blaminsky ; commissioning editor Edward Gordon ; copy editors Puja Lalwani, Alfida Paiva, Laxmi Subramanian

Label
Hadoop mapreduce v2 cookbook : explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets
Title
Hadoop mapreduce v2 cookbook
Title remainder
explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets
Statement of responsibility
Thilina Gunarathne ; cover image by Jarek Blaminsky ; commissioning editor Edward Gordon ; copy editors Puja Lalwani, Alfida Paiva, Laxmi Subramanian
Creator
Contributor
Author
Cover designer
Editor
Subject
Genre
Language
eng
Summary
If you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux
Member of
Cataloging source
E7B
http://library.link/vocab/creatorName
Gunarathne, Thilina
Dewey number
004.36
Illustrations
illustrations
Index
index present
LC call number
QA76.9.D5
LC item number
.G863 2015
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Jarek Blaminsky
  • Gordon, Edward
  • Lalwani, Puja
  • Paiva, Alfida
  • Subramanian, Laxmi
Series statement
Community experience distilled
http://library.link/vocab/subjectName
  • Electronic data processing
  • File organization (Computer science)
Label
Hadoop mapreduce v2 cookbook : explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets, Thilina Gunarathne ; cover image by Jarek Blaminsky ; commissioning editor Edward Gordon ; copy editors Puja Lalwani, Alfida Paiva, Laxmi Subramanian
Link
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=959553
Instantiates
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Author; Acknowledgments; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Hadoop v2; Introduction; Setting up Hadoop v2 on your local machine; Writing a WordCount MapReduce application, bundling it, and running it using Hadoop local mode; Adding a combiner step to the WordCount MapReduce program; Setting up HDFS; Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2; Setting up Hadoop ecosystem in a distributed cluster environment using a Hadoop distribution
  • HDFS command-line file operationsRunning the WordCount program in a distributed cluster environment; Benchmarking HDFS using DFSIO; Benchmarking Hadoop MapReduce using TeraSort; Chapter 2: Cloud Deployments -- Using Hadoop YARN on Cloud Environments; Introduction; Running Hadoop MapReduce v2 computations using Amazon Elastic MapReduce; Saving money using Amazon EC2 Spot Instances to execute EMR job flows; Executing a Pig script using EMR; Executing a Hive script using EMR; Creating an Amazon EMR job flow using the AWS Command Line Interface
  • Deploying an Apache HBase cluster on Amazon EC2 using EMRUsing EMR bootstrap actions to configure VMs for the Amazon EMR jobs; Using Apache Whirr to deploy an Apache Hadoop cluster in a cloud environment; Chapter 3: Hadoop Essentials -- Configurations, Unit Tests, and Other APIs; Introduction; Optimizing Hadoop YARN and MapReduce configurations for cluster deployments; Shared user Hadoop clusters -- using Fair and Capacity schedulers; Setting classpath precedence to user-provided JARs; Speculative execution of straggling tasks; Unit testing Hadoop MapReduce applications using MRUnit
  • Integration testing Hadoop MapReduce applications using MiniYarnClusterAdding a new DataNode; Decommissioning DataNodes; Using multiple disks/volumes and limiting HDFS disk usage; Setting the HDFS block size; Setting the file replication factor; Using the HDFS Java API; Chapter 4: Developing Complex Hadoop MapReduce Applications; Introduction; Choosing appropriate Hadoop data types; Implementing a custom Hadoop Writable data type; Implementing a custom Hadoop key type; Emitting data of different value types from a Mapper; Choosing a suitable Hadoop InputFormat for your input data format
  • Adding support for new input data formats -- implementing a custom InputFormatFormatting the results of MapReduce computations -- using Hadoop OutputFormats; Writing multiple outputs from a MapReduce computation; Hadoop intermediate data partitioning; Secondary sorting -- sorting Reduce input values; Broadcasting and distributing shared resources to tasks in a MapReduce job -- Hadoop DistributedCache; Using Hadoop with legacy applications -- Hadoop Streaming; Adding dependencies between MapReduce jobs; Hadoop counters for reporting custom metrics; Chapter 5: Analytics; Introduction
Dimensions
unknown
Edition
Second edition.
Extent
1 online resource (322 pages)
File format
unknown
Form of item
online
Isbn
9781783285488
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
eBooks on EBSCOhost
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)905919092
  • (OCoLC)ocn905919092
Label
Hadoop mapreduce v2 cookbook : explore the hadoop mapreduce v2 ecosystem to gain insights from very large datasets, Thilina Gunarathne ; cover image by Jarek Blaminsky ; commissioning editor Edward Gordon ; copy editors Puja Lalwani, Alfida Paiva, Laxmi Subramanian
Link
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=959553
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Author; Acknowledgments; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Hadoop v2; Introduction; Setting up Hadoop v2 on your local machine; Writing a WordCount MapReduce application, bundling it, and running it using Hadoop local mode; Adding a combiner step to the WordCount MapReduce program; Setting up HDFS; Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2; Setting up Hadoop ecosystem in a distributed cluster environment using a Hadoop distribution
  • HDFS command-line file operationsRunning the WordCount program in a distributed cluster environment; Benchmarking HDFS using DFSIO; Benchmarking Hadoop MapReduce using TeraSort; Chapter 2: Cloud Deployments -- Using Hadoop YARN on Cloud Environments; Introduction; Running Hadoop MapReduce v2 computations using Amazon Elastic MapReduce; Saving money using Amazon EC2 Spot Instances to execute EMR job flows; Executing a Pig script using EMR; Executing a Hive script using EMR; Creating an Amazon EMR job flow using the AWS Command Line Interface
  • Deploying an Apache HBase cluster on Amazon EC2 using EMRUsing EMR bootstrap actions to configure VMs for the Amazon EMR jobs; Using Apache Whirr to deploy an Apache Hadoop cluster in a cloud environment; Chapter 3: Hadoop Essentials -- Configurations, Unit Tests, and Other APIs; Introduction; Optimizing Hadoop YARN and MapReduce configurations for cluster deployments; Shared user Hadoop clusters -- using Fair and Capacity schedulers; Setting classpath precedence to user-provided JARs; Speculative execution of straggling tasks; Unit testing Hadoop MapReduce applications using MRUnit
  • Integration testing Hadoop MapReduce applications using MiniYarnClusterAdding a new DataNode; Decommissioning DataNodes; Using multiple disks/volumes and limiting HDFS disk usage; Setting the HDFS block size; Setting the file replication factor; Using the HDFS Java API; Chapter 4: Developing Complex Hadoop MapReduce Applications; Introduction; Choosing appropriate Hadoop data types; Implementing a custom Hadoop Writable data type; Implementing a custom Hadoop key type; Emitting data of different value types from a Mapper; Choosing a suitable Hadoop InputFormat for your input data format
  • Adding support for new input data formats -- implementing a custom InputFormatFormatting the results of MapReduce computations -- using Hadoop OutputFormats; Writing multiple outputs from a MapReduce computation; Hadoop intermediate data partitioning; Secondary sorting -- sorting Reduce input values; Broadcasting and distributing shared resources to tasks in a MapReduce job -- Hadoop DistributedCache; Using Hadoop with legacy applications -- Hadoop Streaming; Adding dependencies between MapReduce jobs; Hadoop counters for reporting custom metrics; Chapter 5: Analytics; Introduction
Dimensions
unknown
Edition
Second edition.
Extent
1 online resource (322 pages)
File format
unknown
Form of item
online
Isbn
9781783285488
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
eBooks on EBSCOhost
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)905919092
  • (OCoLC)ocn905919092

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