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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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 Drabas Drabas
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Drabas
 Lee Lee
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Lee
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Table of Contents (20) Chapters Close

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Chapter 7. GraphFrames

Graphs are an interesting way to solve data problems because graph structures are a more intuitive approach to many classes of data problems.

In this chapter, you will learn about:

  • Why use graphs?

  • Understanding the classic graph problem: the flights dataset

  • Understanding the graph vertices and edges

  • Simple queries

  • Using motif finding

  • Using breadth first search

  • Using PageRank

  • Visualizing flights using D3

Whether traversing social networks or restaurant recommendations, it is easier to understand these data problems within the context of graph structures: vertices, edges, and properties:

For example, within the context of social networks, the vertices are the people while the edges are the connections between them. Within the context of restaurant recommendations, the vertices (for example) involve the location, cuisine type, and restaurants while the edges are the connections between them (for example, these three restaurants are in Vancouver, BC, but only two of them serve ramen...

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