PySpark Recipes

1. Front Matter

   Pages i-xxiii

   PDF

2. The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks

   • Raju Kumar Mishra

   Pages 1-14

3. Installation

   • Raju Kumar Mishra

   Pages 15-44

4. Introduction to Python and NumPy

   • Raju Kumar Mishra

   Pages 45-83

5. Spark Architecture and the Resilient Distributed Dataset

   • Raju Kumar Mishra

   Pages 85-114

6. The Power of Pairs: Paired RDDs

   • Raju Kumar Mishra

   Pages 115-136

7. I/O in PySpark

   • Raju Kumar Mishra

   Pages 137-161

8. Optimizing PySpark and PySpark Streaming

   • Raju Kumar Mishra

   Pages 163-185

9. PySparkSQL

   • Raju Kumar Mishra

   Pages 187-233

10. PySpark MLlib and Linear Regression

    • Raju Kumar Mishra

    Pages 235-259

11. Back Matter

    Pages 261-265

    PDF

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

• Big Data
• Spark
• Python
• Numpy
• Scipy
• Resilient Distributed Database
• Advanced PySpark
• Spark SQL
• MLLIb
• PySpark2

• Bangalore, India

  Raju Kumar Mishra

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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