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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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Toc

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

Chapter 15.  Understanding Data Processing using Apache Spark

In this chapter, we will present the main features of data processing architecture and the Cloudera platform distribution. Then, we will explore how to use a distributed filesystem and how to managing files from terminal and using a web interface. Finally, we will describe the use of Apache Spark, which is an open source, big data processing framework built with the goal of being fast and easy to use. Apache Spark provides us with a unified framework to manage big data processing requirements, such as data streaming, machine learning, and analytics.

In this chapter, we will cover these topics:

  • Understanding data processing
  • Platform for data processing
  • An introduction to the distributed file system
  • An introduction to Apache Spark
  • Understanding data processing

Since the first edition of this book in 2013, there has been big changes in the data-driven scene. With the emerge of buzzwords such as big data, data science, and deep...

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