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PySpark Recipes

A Problem-Solution Approach with PySpark2

  • Book
  • © 2018
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Overview

Authors:
  1. Raju Kumar Mishra

    1. Bangalore, India

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  • Presents advanced features of PySpark and code optimization techniques
  • Covers SparkSQL, Spark Streaming, Spark MLlib, and GraphFrames
  • Discusses and demonstrates Data Science and Big Data processing with PySpark MLlib

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks

    • Raju Kumar Mishra
    Pages 1-14
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  3. Installation

    • Raju Kumar Mishra
    Pages 15-44
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  4. Introduction to Python and NumPy

    • Raju Kumar Mishra
    Pages 45-83
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  5. Spark Architecture and the Resilient Distributed Dataset

    • Raju Kumar Mishra
    Pages 85-114
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  6. The Power of Pairs: Paired RDDs

    • Raju Kumar Mishra
    Pages 115-136
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  7. I/O in PySpark

    • Raju Kumar Mishra
    Pages 137-161
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  8. Optimizing PySpark and PySpark Streaming

    • Raju Kumar Mishra
    Pages 163-185
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  9. PySparkSQL

    • Raju Kumar Mishra
    Pages 187-233
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  10. PySpark MLlib and Linear Regression

    • Raju Kumar Mishra
    Pages 235-259
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  11. Back Matter

    Pages 261-265
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Keywords

About this book

Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved!


PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model.



What You Will Learn  
  • Understand the advanced features of PySpark2 and SparkSQL
  • Optimize your code
  • Program SparkSQL with Python
  • Use Spark Streaming and Spark MLlib with Python
  • Perform graph analysis with GraphFrames



Who This Book Is For


Data analysts, Python programmers, big data enthusiasts



Authors and Affiliations

  • Bangalore, India

    Raju Kumar Mishra

About the author

Raju Mishra has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others.


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