Learning Big Data with Amazon Elastic MapReduce. (Record no. 2780556)

MARC details
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fixed length control field 141107s2014 xx o 000 0 eng d
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Original cataloging agency IDEBK
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Record control number 018005576
Source Uk
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International Standard Book Number 1322242186
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781322242187
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International Standard Book Number 9781782173441
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International Standard Book Number 1782173447
Qualifying information (electronic bk.)
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International Standard Book Number 9781782173434
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System control number (OCoLC)894628948
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Stock number 655498
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050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number T55.4-60.8
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082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 004.109236
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Singh, Amarkant
Titles and other words associated with a name (Software developer)
9 (RLIN) 1036541
245 10 - TITLE STATEMENT
Title Learning Big Data with Amazon Elastic MapReduce.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Packt Publishing,
Date of publication, distribution, etc 2014.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource
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520 ## - SUMMARY, ETC.
Summary, etc Amazon Elastic MapReduce is a web service used to process and store vast amount of data, and it is one of the largest Hadoop operators in the world. With the increase in the amount of data generated and collected by many businesses and the arrival of cost-effective cloud-based solutions for distributed computing, the feasibility to crunch large amounts of data to get deep insights within a short span of time has increased greatly. This book will get you started with AWS so that you can quickly create your own account and explore the services provided, many of which you might be delighted to use. This book covers the architectural details of the MapReduce framework, Apache Hadoop, various job models on EMR, how to manage clusters on EMR, and the command-line tools available with EMR. Each chapter builds on the knowledge of the previous one, leading to the final chapter where you will learn about solving a real-world use case using Apache Hadoop and EMR. This book will, therefore, get you up and running with major Big Data technologies quickly and efficiently.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Cover; Copyright; Credits; About the Authors; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Amazon Web Services; What is Amazon Web Services?; Structure and Design; Regions; Availability Zones; Services provided by AWS; Compute; Amazon EC2; Auto Scaling; Elastic Load Balancing; Amazon Workspaces; Storage; Amazon S3; Amazon EBS; Amazon Glacier; AWS Storage Gateway; AWS Import/Export; Databases; Amazon RDS; Amazon DynamoDB; Amazon Redshift; Amazon ElastiCache; Networking and CDN; Amazon VPC; Amazon Route 53; Amazon CloudFront; AWS Direct Connect
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note AnalyticsAmazon EMR; Amazon Kinesis; AWS Data Pipeline; Application services; Amazon CloudSearch (Beta); Amazon SQS; Amazon SNS; Amazon SES; Amazon AppStream; Amazon Elastic Transcoder; Amazon SWF; Deployment and Management; AWS Identity and Access Management; Amazon CloudWatch; AWS Elastic Beanstalk; AWS CloudFormation; AWS OpsWorks; AWS CloudHSM; AWS CloudTrail; AWS Pricing; Creating an account on AWS; Step 1 -- Creating an Amazon.com account; Step 2 -- Providing a payment method; Step 3 -- Identity verification by telephone; Step 4 -- Selecting the AWS support plan
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Launching the AWS management consoleGetting started with Amazon EC2; How to start a machine on AWS?; Step 1 -- Choosing an Amazon Machine Image; Step 2 -- Choosing an instance type; Step 3 -- Configuring instance details; Step 4 -- Adding storage; Step 5 -- Tagging your instance; Step 6 -- Configuring a security group; Communicating with the launched instance; EC2 instance types; General purpose; Memory optimized; Compute optimized; Getting started with Amazon S3; Creating a S3 bucket; Bucket naming; S3cmd; Summary; Chapter 2: MapReduce; The map function; The reduce function; Divide and conquer
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note What is MapReduce?The map reduce function models; The map function model; The reduce function model; Data life cycle in the MapReduce framework; Creation of input data splits; Record reader; Mapper; Combiner; Partitioner; Shuffle and sort; Reducer; Real-world examples and use cases of MapReduce; Social networks ; Media and entertainment; E-commerce and websites; Fraud detection and financial analytics; Search engines and ad networks; ETL and data analytics; Software distributions built on the MapReduce framework; Apache Hadoop; MapR; Cloudera distribution; Summary; Chapter 3: Apache Hadoop
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note What is Apache Hadoop?Hadoop modules; Hadoop Distributed File System; Major architectural goals of HDFS; Block replication and rack awareness; The HDFS architecture; NameNode; DataNode; Apache Hadoop MapReduce; Hadoop MapReduce 1.x; JobTracker; TaskTracker; Hadoop MapReduce 2.0; Hadoop YARN; Apache Hadoop as a platform; Apache Pig; Apache Hive; Summary; Chapter 4: Amazon EMR -- Hadoop on Amazon Web Services; What is AWS EMR?; Features of EMR; Accessing Amazon EMR features; Programming on AWS EMR; The EMR architecture; Types of nodes; EMR Job Flow and Steps; Job Steps; An EMR cluster
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Hadoop filesystem on EMR -- S3 and HDFS
590 ## - LOCAL NOTE (RLIN)
Local note eBooks on EBSCOhost
Provenance (VM) [OBSOLETE] EBSCO eBook Subscription Academic Collection - Worldwide
630 00 - SUBJECT ADDED ENTRY--UNIFORM TITLE
Uniform title MapReduce (Computer file)
9 (RLIN) 376905
630 07 - SUBJECT ADDED ENTRY--UNIFORM TITLE
Uniform title MapReduce (Computer file)
Source of heading or term fast
-- (OCoLC)fst01915150
9 (RLIN) 376905
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Topical term or geographic name as entry element Big data.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Données volumineuses.
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General subdivision Computer Literacy.
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Topical term or geographic name as entry element COMPUTERS
General subdivision Data Processing.
Source of heading or term bisacsh
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650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS
General subdivision Hardware
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Topical term or geographic name as entry element COMPUTERS
General subdivision Information Technology.
Source of heading or term bisacsh
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Topical term or geographic name as entry element COMPUTERS
General subdivision Machine Theory.
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Topical term or geographic name as entry element Big data.
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Genre/form data or focus term Electronic books.
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Genre/form data or focus term Electronic books.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Note Druck-Ausgabe
Title Singh, Amarkant. Learning Big Data with Amazon Elastic MapReduce
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=882358">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=882358</a>
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Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Current library Date acquired Total Checkouts Date last seen Price effective from Koha item type
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