In Hadoop, and for general big data, analytics is the key interest area, as Hadoop is a powerful tool to process complex programs and algorithms to improve the process and business. Data analytics can identify deep insights and can help to optimize the process and stay ahead in the competition. Due to the powerful processing nature of Hadoop, machine learning has been in focus and a lot of development in the algorithms and techniques have been adapted for Hadoop. Machine learning techniques are also used in predictive analytics. Data analytics and machine learning is needed by competitive organizations to stay ahead in the competition and by some researchers, especially in life sciences, to process genes and medical records' patterns to generate much important and useful insights and details that are quite necessary in the medical field. This is also needed by researchers in the field of robotics to provide intelligence to machines for performing and optimizing...
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