As we have already discussed about Hadoop being a Batch processing system and some data source types that varies in their velocity or rate, volume of data. Many system especially machines generates a lot of data consistently, they need to process such high volume data to maintain quality and avoid heavy loss and thus the need for Stream processing has emerged. To design systems that are built as Lambda implementation, which are Batch as well as Stream processing systems, We should have combination of different environment that can integrate with each other to process the data and quite obviously which increases the complexity of designing the system. Streaming data is complex to store, analyze, process, and maintain. Prior to version 2.x, Hadoop was only a Batch processing system, and after the emergence of YARN and other frameworks and the integration of those frameworks with YARN, Hadoop can be designed for streaming and real...
Hadoop Essentials
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Hadoop Essentials
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Overview of this book
This book jumps into the world of Hadoop and its tools, to help you learn how to use them effectively to optimize and improve the way you handle Big Data.
Starting with the fundamentals Hadoop YARN, MapReduce, HDFS, and other vital elements in the Hadoop ecosystem, you will soon learn many exciting topics such as MapReduce patterns, data management, and real-time data analysis using Hadoop. You will also explore a number of the leading data processing tools including Hive and Pig, and learn how to use Sqoop and Flume, two of the most powerful technologies used for data ingestion. With further guidance on data streaming and real-time analytics with Storm and Spark, Hadoop Essentials is a reliable and relevant resource for anyone who understands the difficulties - and opportunities - presented by Big Data today.
With this guide, you'll develop your confidence with Hadoop, and be able to use the knowledge and skills you learn to successfully harness its unparalleled capabilities.
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Table of Contents (15 chapters)
Hadoop Essentials
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to Big Data and Hadoop
Hadoop Ecosystem
Pillars of Hadoop – HDFS, MapReduce, and YARN
Data Access Components – Hive and Pig
Storage Component – HBase
Data Ingestion in Hadoop – Sqoop and Flume
Streaming and Real-time Analysis – Storm and Spark
Index
Customer Reviews