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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles FREE CHAPTER 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Apache Storm

Apache Storm is a free and open source distributed real-time stream processing framework. At the time of writing this book, the stable release version of Apache Storm is 1.0.5. The Storm framework is predominantly written in the Clojure programming language. Originally, it was created and developed by Nathan Marz and the team at Backtype. The project was later acquired by Twitter.

During one of his talks on the Storm framework, Nathan Marz talked about stream processing applications using any framework, such as Storm. These applications involved queues and worker threads. Some of the data source threads write messages to queues and other threads pick up these messages and write to target data stores. The main drawback here is that source threads and targets threads do not match the data load of each other and this results in data pileup. It also results in data loss...

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