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Modern Big Data Processing with Hadoop

You're reading from  Modern Big Data Processing with Hadoop

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Pages 394 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
V Naresh Kumar V Naresh Kumar
Profile icon V Naresh Kumar
Manoj R Patil Manoj R Patil
Profile icon Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Profile icon Prashant Shindgikar
View More author details
Toc

Table of Contents (12) Chapters close

Preface 1. Enterprise Data Architecture Principles 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

Hadoop file formats

In Hadoop, there are many file formats available. A user can select any format based on the use case. Each format has special features in terms of storage and performance. Let's discuss each file format in detail.

Text/CSV file

Text and CSV files are very common in Hadoop data processing algorithms. Each line in the file is treated as a new record. Typically, each line ends with the n character. These files do not support column headers. Hence, while processing, an extra line of the code is always required to remove column headings. CSV files are typically compressed using GZIP codec because they do not support block level compression; it adds to more processing costs. Needless to mention they do not...

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