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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

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781786463708

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Data Processing

Authors (2):

Denny Lee

Tomasz Drabas

View More author details

Table of Contents (13) Chapters

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

Transformations

Transformations shape your dataset. These include mapping, filtering, joining, and transcoding the values in your dataset. In this section, we will showcase some of the transformations available on RDDs.

Note

Due to space constraints we include only the most often used transformations and actions here. For a full set of methods available we suggest you check PySpark's documentation on RDDs http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.

Since RDDs are schema-less, in this section we assume you know the schema of the produced dataset. If you cannot remember the positions of information in the parsed dataset we suggest you refer to the definition of the extractInformation(...) method on GitHub, code for Chapter 03.

The .map(...) transformation

It can be argued that you will use the .map(...) transformation most often. The method is applied to each element of the RDD: In the case of the data_from_file_conv dataset, you can think of this as a transformation...

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Authors (2)

Drabas

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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Lee

Lee

Joanna Lee has more than 8 years of experience in game development. She has designed and programmed various video games. She first started working with Unreals game engine in 2005 and is very excited to be able to author a book about the newest Unreal Engine 4. She has also worked with many other engines as well as reviewed books and videos on Cry Engine 4.

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