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Mastering Hadoop 3

You're reading from  Mastering Hadoop 3

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Pages 544 pages
Edition 1st Edition
Languages
Authors (2):
Chanchal Singh Chanchal Singh
Profile icon Chanchal Singh
Manish Kumar Manish Kumar
Profile icon Manish Kumar
View More author details
Toc

Table of Contents (23) Chapters close

Title Page
Dedication
About Packt
Foreword
Contributors
Preface
1. Journey to Hadoop 3 2. Deep Dive into the Hadoop Distributed File System 3. YARN Resource Management in Hadoop 4. Internals of MapReduce 5. SQL on Hadoop 6. Real-Time Processing Engines 7. Widely Used Hadoop Ecosystem Components 8. Designing Applications in Hadoop 9. Real-Time Stream Processing in Hadoop 10. Machine Learning in Hadoop 11. Hadoop in the Cloud 12. Hadoop Cluster Profiling 13. Who Can Do What in Hadoop 14. Network and Data Security 15. Monitoring Hadoop 1. Other Books You May Enjoy Index

What are streaming datasets?


Streaming datasets are about doing data processing, not on bounded data, but on unbounded data. Typical datasets are bounded. That means they are complete. At the very least, you will process data as if it were complete. Realistically, we know that there will always be new data, but as far as data processing is concerned, we will treat it as if it were a complete dataset. In the case of bounded data, data processing is done in phases and until and unless one phase is complete, other phases of data processing do not start. Another way to think about bounded data processing is that we will be done analyzing the data before new data comes in. Bounded datasets are finite in size. The following diagram represents how bounded data is processed using a typical MapReduce batch processing engine:

On the other hand, if you have an unbounded dataset (also known as an infinite dataset), it is never complete; there is always new data coming in, and typically, data is coming...

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