文件名称：Hadoop.Essentials.1784396680
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更新时间：2018-05-27 08:02:22
Hadoop
Title: Hadoop Essentials Author: Shiva Achari Length: 172 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2015-04-24 ISBN-10: 1784396680 ISBN-13: 9781784396688 Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem About This Book Get to grips with different Hadoop ecosystem tools that can help you achieve scalability, performance, maintainability, and efficiency in your projects Understand the different paradigms of Hadoop and get the most out of it to engage the power of your data This is a fast-paced reference guide covering the key components and functionalities of Hadoop Who This Book Is For If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. This book is also meant for Hadoop professionals who want to find solutions to the different challenges they come across in their Hadoop projects. In Detail This book jumps into the world of Hadoop ecosystem components and its tools in a simplified manner, and provides you with the skills to utilize them effectively for faster and effective development of Hadoop projects. Starting with the concepts of Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, you will soon learn many exciting topics such as MapReduce patterns, data management, and real-time data analysis using Hadoop. You will also get acquainted with many Hadoop ecosystem components tools such as Hive, HBase, Pig, Sqoop, Flume, Storm, and Spark. By the end of the book, you will be confident to begin working with Hadoop straightaway and implement the knowledge gained in all your real-world scenarios. Table of Contents Chapter 1: Introduction To Big Data And Hadoop Chapter 2: Hadoop Ecosystem Chapter 3: Pillars Of Hadoop – Hdfs, Mapreduce, And Yarn Chapter 4: Data Access Components – Hive And Pig Chapter 5: Storage Component – Hbase Chapter 6: Data Ingestion In Hadoop – Sqoop And Flume Chapter 7: Streaming And Real-Time Analysis – Storm And Spark