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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Introduction

Graphs enable solving certain data problems more easily and intuitively. At the core of a graph lies concepts of edges, nodes (or vertices), and their properties. For example, the following are two seemingly disconnected graphs. The left one represents a social network and the relationship (the edges of the graph) between friends (the vertices of the graph), while the right one is a graph that represents restaurant recommendations. Note that the vertices for our restaurant recommendations are not only the restaurants themselves but also the cuisine type (for example, Ramen) and location (for example, Vancouver, B.C., Canada); these are the properties of the vertices. This ability to assign nodes to virtually anything and use edges to define the relationship between these nodes is the greatest virtue of graphs, that is, their flexibility:

This flexibility allows...

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