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Hadoop Essentials

You're reading from   Hadoop Essentials Delve into the key concepts of Hadoop and get a thorough understanding of the Hadoop ecosystem

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784396688
Length 194 pages
Edition 1st Edition
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Author (1):
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Shiva Achari Shiva Achari
Author Profile Icon Shiva Achari
Shiva Achari
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Big Data and Hadoop FREE CHAPTER 2. Hadoop Ecosystem 3. Pillars of Hadoop – HDFS, MapReduce, and YARN 4. Data Access Components – Hive and Pig 5. Storage Component – HBase 6. Data Ingestion in Hadoop – Sqoop and Flume 7. Streaming and Real-time Analysis – Storm and Spark Index

An introduction to Storm


Storm can process streaming data really fast (clocked at over one million messages per second per node); it is scalable (thousands of worker nodes of cluster), fault tolerant, and reliable (message processing is guaranteed). Storm is easy to use and deploy, which also eases its maintainability. Hadoop is primarily designed for batch processing and for Lambda Architecture systems. Storm is well-integrated with Hadoop, in order to provide distributed real-time streaming analysis reliably with good fault tolerance for big data.

Storm was developed by Twitter and later contributed to Apache. Storm's benchmark results are quite outstanding at over a million sets of data called tuples processed per second per node. Storm utilizes a Thrift interface; hence, the client can be written in any language and even non-JVM language communicates over JSON-based protocol. Considering the complexity of Storm, it is a fairly easy-to-use API.

Features of Storm

Some important features of...

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