Programming Elastic MapReduce: Using AWS Services to Build an End-to-End Application (Paperback)

Kevin Schmidt, Christopher Phillips

  • 出版商: O'Reilly
  • 出版日期: 2014-01-28
  • 定價: $1,200
  • 售價: 9.5$1,140
  • 貴賓價: 9.0$1,080
  • 語言: 英文
  • 頁數: 174
  • 裝訂: Paperback
  • ISBN: 1449363628
  • ISBN-13: 9781449363628
  • 相關分類: Amazon Web Services分散式架構
  • 立即出貨 (庫存=1)



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


儘管使用Apache Hadoop處理大量數據不需要大型計算基礎設施,但要開始使用仍然可能很困難。這本實用指南將向您展示如何通過使用Amazon Elastic MapReduce(EMR)在Amazon Web Services(AWS)中的托管Hadoop框架,快速啟動雲端數據分析項目。

作者Kevin Schmidt和Christopher Phillips通過引導您完成構建示例MapReduce日誌分析應用程序的過程,展示了使用EMR和各種AWS和Apache技術的最佳實踐。通過代碼示例和示例配置,您將學習如何組合必要的構建塊來解決您最大的數據分析問題。

  • 瞭解在大規模數據分析中使用的AWS和Apache軟件工具的概述

  • 進行使用簡單日誌分析器執行作業流程的過程

  • 發現用於過濾和分析數據集的有用MapReduce模式

  • 使用Apache Hive和Pig而不是Java來構建MapReduce作業流程

  • 學習使用Amazon EMR運行機器學習算法的基礎知識

  • 為使用Amazon EMR和其他AWS工具開發項目成本模型