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Mastering Hadoop 3

You're reading from   Mastering Hadoop 3 Big data processing at scale to unlock unique business insights

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Length 544 pages
Edition 1st Edition
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Authors (3):
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Timothy Wong Timothy Wong
Author Profile Icon Timothy Wong
Timothy Wong
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
Chanchal Singh Chanchal Singh
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Chanchal Singh
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Hadoop 3 FREE CHAPTER
2. Journey to Hadoop 3 3. Deep Dive into the Hadoop Distributed File System 4. YARN Resource Management in Hadoop 5. Internals of MapReduce 6. Section 2: Hadoop Ecosystem
7. SQL on Hadoop 8. Real-Time Processing Engines 9. Widely Used Hadoop Ecosystem Components 10. Section 3: Hadoop in the Real World
11. Designing Applications in Hadoop 12. Real-Time Stream Processing in Hadoop 13. Machine Learning in Hadoop 14. Hadoop in the Cloud 15. Hadoop Cluster Profiling 16. Section 4: Securing Hadoop
17. Who Can Do What in Hadoop 18. Network and Data Security 19. Monitoring Hadoop 20. Other Books You May Enjoy

File formats

Files have been in data processing for decades. A file acts as a persistent storage for data, and programmers have been using files for exchanging or storing data  for a long time now. There are formats associated with each file that tell you how data is being written and read from the file. For example, if we consider the .csv file, we may assume that each record is separated by a line and each column is separated by a delimiter. If the writer does not put data in the specific agreed format, then the reader may read data wrongly or processing logic may break. Each file format has a specific way of storing the data and there are different parameters that can help you decide which file format is best suited for your application. In this section, we will walk you through some of the widely used file formats and their internals. 

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