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Mastering Apache Storm


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English | 16 Aug. 2017 | ASIN: B074M54PXB | ISBN: 1787125637 | 284 Pages | AZW3 | 3.31 MB

Key Features

Exploit the various real-time processing functionalities offered by Apache Storm such as parallelism, data partitioning, and more
Integrate Storm with other Big Data technologies like Hadoop, HBase, and Apache Kafka
An easy-to-understand guide to effortlessly create distributed applications with Storm

Book Description

Apache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm.

The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You'll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we'll introduce you to Trident and you'll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm.

With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs.

What you will learn

Understand the core concepts of Apache Storm and real-time processing
Follow the steps to deploy multiple nodes of Storm Cluster
Create Trident topologies to support various message-processing semantics
Make your cluster sharing effective using Storm scheduling
Integrate Apache Storm with other Big Data technologies such as Hadoop, HBase, Kafka, and more
Monitor the health of your Storm cluster

About the Author

Ankit Jain holds a bachelor's degree in computer science and engineering. He has 6 years, experience in designing and architecting solutions for the big data domain and has been involved with several complex engagements. His technical strengths include Hadoop, Storm, S4, HBase, Hive, Sqoop, Flume, Elasticsearch, machine learning, Kafka, Spring, Java, and J2EE.

He also shares his thoughts on his personal blog. You can follow him on Twitter at @mynameisanky. He spends most of his time reading books and playing with different technologies. When not at work, he spends time with his family and friends watching movies and playing games.

Table of Contents

Real-Time Processing and Storm Introduction
Deploying Storm in Cluster
Storm Parallelism and Data Partitioning
Trident Introduction
Trident Topology and Uses
Storm Scheduler
Monitoring of Storm Cluster
Integration of Storm and Kafka
Storm and Hadoop Integration
Storm Integration with Redis, Elasticsearch and HBase
Apache Log Processing
Twitter Tweets Collection and Machine learning
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