Book Image

Hadoop Essentials

By : Shiva Achari
Book Image

Hadoop Essentials

By: Shiva Achari

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.
Table of Contents (15 chapters)
Hadoop Essentials
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
3
Pillars of Hadoop – HDFS, MapReduce, and YARN
Index

Summary


In this chapter, we have discussed HDFS, MapReduce, and YARN in detail.

HDFS is highly scalable, fault tolerant, reliable, and portable, and is designed to work even on commodity hardwares. HDFS architecture has four daemon processes, which are NameNode, DataNode, Checkpoint NameNode, and Backup Node. HDFS has a lot of complex design challenges, which are managed by different techniques such as Replication, Heartbeat, Block concept, Rack Awareness, and Block Scanner, and HDFS Federation makes HDFS highly available and fault tolerant.

Hadoop MapReduce is also highly scalable, fault tolerant, and designed to work even in commodity hardwares. MapReduce architecture has a master JobTracker and multiple worker TaskTracker processes in the Nodes. MapReduce jobs are broken into multistep processes, which are Mapper, Shuffle, Sort, Reducer, and auxiliary Combiner and Partitioner. MapReduce jobs needs a lot of data transfer, for which Hadoop uses Writable and WritableComparable interfaces....