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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

An introduction to the distributed file system

A distributed file system is practically the same as any file system due to its basic actions such as storing, reading, deleting files, and assigning security levels are support. The main difference is focused on the number of servers that can be used at same time without dealing with complexity of synchronization. In this case, we can store large files in different server nodes without caring about redundancy or parallel operations.

There are a lot of frameworks for distributed file systems, such as Red Hat Cluster FS, Ceph File system, Hadoop Distributed File System (HDFS), and Tachyon File System.

In this chapter, we will use HDFS, which is an open source implementation of Google File System, built to handle large files into a cluster of commodity hardware. The HDFS cluster implements a NameNode that manages operations through the file system, and a series of DataNodes that manage the storage of the files in the cluster nodes individually...

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