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Learning PySpark

You're reading from Learning PySpark

Product type Book

Published in Feb 2017

Publisher Packt

ISBN-13 9781786463708

Pages 274 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Tomasz Drabas

Denny Lee

View More author details

Table of Contents (20) Chapters

Learning PySpark

Credits

Foreword

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Understanding Spark

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

Introducing GraphFrames

GraphFrames utilizes the power of Apache Spark DataFrames to support general graph processing. Specifically, the vertices and edges are represented by DataFrames allowing us to store arbitrary data with each vertex and edge. While GraphFrames is similar to Spark's GraphX library, there are some key differences, including:

GraphFrames leverage the performance optimizations and simplicity of the DataFrame API.
By using the DataFrame API, GraphFrames now have Python, Java, and Scala APIs. GraphX is only accessible through Scala; now all its algorithms are available in Python and Java.
Note, at the time of writing, there was a bug preventing GraphFrames from working with Python3.x, hence we will be using Python2.x.

At the time of writing, GraphFrames is on version 0.3 and available as a Spark package (http://spark-packages.org) at https://spark-packages.org/package/graphframes/graphframes.

Tip

For more information about GraphFrames, please refer to Introducing GraphFra mes...

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Authors (2)

Tomasz Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

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Denny Lee

Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB teamMicrosoft's blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incubation team that built Microsoft's Hadoop on Windows and Azure service (currently known as HDInsight). Denny also has a Masters in Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers for the last 15 years.

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