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Cloudera Hadoop大数据平台实战指南
作者：宋立桓、陈建平
出版社：清华大学出版社
ISBN：9787302517535
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Hadoop 权威指南第四版(Hadoop.The Definitive Guide 4th Edition) 评分:
Hadoop权威指南第四版(英文版)，这是最新的版本，比第三版多介绍了 YARN，Parquet,Flume,Crunch,Spark框架，并比第三版多了两个项目示例，如Hadoop在健康系统和基因工程中的应用。英文版比中文版对Hadooop的理解更深刻。中文翻译见:http://www.jianshu.com/c/6bcc828f94c8
上传时间：2017-08 大小：9.65MB
24.13MBHadoop: The Definitive Guide 4th
2018-06-02Hadoop 权威指南第四版，Hadoop: The Definitive Guide 4th 英文原版，内有 pdf 注释勾划，不喜欢的朋友使用 Adobe reader 删除即可
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2018-05-25Hadoop: The Definitive Guide, 4th Edition Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing. Learn fundamental components such as MapReduce, HDFS, and YARN Explore MapReduce in depth, including steps for developing applications with it Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN Learn two data formats: Avro for data serialization and Parquet for nested data Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer) Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop Learn the HBase distributed database and the ZooKeeper distributed configuration service
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2009-12-03书名：Hadoop The Definitive Guide 语言：英文 The rest of this book is organized as follows. Chapter 2 provides an introduction to MapReduce. Chapter 3 looks at Hadoop filesystems, and in particular HDFS, in depth. Chapter 4 covers the fundamentals of I/O in Hadoop: data integrity, compression, serialization, and file-based data structures. The next four chapters cover MapReduce in depth. Chapter 5 goes through the practical steps needed to develop a MapReduce application. Chapter 6 looks at how MapReduce is implemented in Hadoop, from the point of view of a user. Chapter 7 is about the MapReduce programming model, and the various data formats that MapReduce can work with. Chapter 8 is on advanced MapReduce topics, including sorting and joining data. Chapters 9 and 10 are for Hadoop administrators, and describe how to set up and maintain a Hadoop cluster running HDFS and MapReduce. Chapters 11, 12, and 13 present Pig, HBase, and ZooKeeper, respectively. Finally, Chapter 14 is a collection of case studies contributed by members of the Apache Hadoop community.
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2009-07-15OReilly.Hadoop.The.Definitive.Guide.June.2009.RETAiL.eBOOk-sUppLeX Description Apache Hadoop is ideal for organizations with a growing need to process massive application datasets. Hadoop: The Definitive Guide is a comprehensive resource for using Hadoop to build reliable, scalable, distributed systems. Programmers will find details for analyzing large datasets with Hadoop, and administrators will learn how to set up and run Hadoop clusters. The book includes case studies that illustrate how Hadoop is used to solve specific problems. Full Description Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters. Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you: * Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduce * Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence * Discover common pitfalls and advanced features for writing real-world MapReduce programs * Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud * Use Pig, a high-level query language for large-scale data processing * Take advantage of HBase, Hadoop's database for structured and semi-structured data * Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject. "Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk." -- Doug Cutting, Hadoop Founder, Yahoo!
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