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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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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

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

Visualizing flights using D3


To get a powerful and fun visualization of the flight paths and connections in this dataset, we can leverage the Airports D3 visualization (https://mbostock.github.io/d3/talk/20111116/airports.html) within our Databricks notebook. By connecting our GraphFrames, DataFrames, and D3 visualizations, we can visualize the scope of all the flight connections as noted for all on-time or early departing flights within this dataset.

The blue circles represent the vertices (that is, airports) where the size of the circle represents the number of edges (that is, flights) in and out of those airports. The black lines are the edges themselves (that is, flights) and their respective connections to the other vertices (that is, airports). Note for any edges that go offscreen, they are representing vertices (that is, airports) in the states of Hawaii and Alaska.

For this to work, we first create a scala package called d3a that is embedded in our notebook (you can download it from...

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