Programming Elastic MapReduce

Name: Programming Elastic MapReduce
Author: Kevin Schmidt

Using AWS Services to Build an End-to-end Application

Specificaties

Paperback, 155 blz. | Engels

O'Reilly | 1e druk, 2014

ISBN13: 9781449363628

Rubricering

Hoofdrubriek : Computer en informatica

Juridisch : Computer en informatica

O'Reilly 1e druk, 2014 9781449363628

Gratis verzonden | Verwachte levertijd ongeveer 16 werkdagen

40,34

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Samenvatting

Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS).

Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, you'll learn how to assemble the building blocks necessary to solve your biggest data analysis problems.

- Get an overview of the AWS and Apache software tools used in large-scale data analysis
- Go through the process of executing a Job Flow with a simple log analyzer
- Discover useful MapReduce patterns for filtering and analyzing data sets
- Use Apache Hive and Pig instead of Java to build a MapReduce Job Flow
- Learn the basics for using Amazon EMR to run machine learning algorithms
- Develop a project cost model for using Amazon EMR and other AWS tools

Specificaties

ISBN13:9781449363628

Taal:Engels

Bindwijze:paperback

Aantal pagina's:155

Uitgever:O'Reilly

Druk:1

Verschijningsdatum:20-12-2013

Hoofdrubriek:Computer en informatica

Inhoudsopgave

Preface

1. Introduction to Amazon Elastic MapReduce
-Amazon Web Services Used in This Book
-Amazon Elastic MapReduce
-Amazon EMR and the Hadoop Ecosystem
-Amazon Elastic MapReduce Versus Traditional Hadoop Installs
-Application Building Blocks

2. Data Collection and Data Analysis with AWS
-Log Analysis Application
-Log Messages as a Data Set for Analytics
-Understanding MapReduce
-Collection Stage
-Simulating Syslog Data
-Developing a MapReduce Application
-Custom JAR MapReduce Job
-Running an Amazon EMR Cluster
-Viewing Our Results
-Debugging a Job Flow
-Our Application and Real-World Uses

3. Data Filtering Design Patterns and Scheduling Work
-Extending the Application Example
-Understanding Web Server Logs
-Finding Errors in the Web Logs Using Data Filtering
-Building Summary Counts in Data Sets
-Job Flow Scheduling
-Scheduling with AWS Data Pipeline
-Real-World Uses

4. Data Analysis with Hive and Pig in Amazon EMR
-Amazon Job Flow Technologies
-What Is Pig?
-Utilizing Pig in Amazon EMR
-What Is Hive?
-Utilizing Hive in Amazon EMR
-Our Application with Hive and Pig

5. Machine Learning Using EMR
-A Quick Tour of Machine Learning
-Python and EMR
-What's Next?

6. Planning AWS Projects and Managing Costs
-Developing a Project Cost Model
-Optimizing AWS Resources to Reduce Project Costs
-Amazon Tools for Estimating Your Project Costs

Appendix A: Amazon Web Services Resources and Tools
Appendix B: Cloud Computing, Amazon Web Services, and Their Impacts
Appendix C: Installation and Setup

Index