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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment 2. Getting Your Big Data into the Spark Environment Using RDDs FREE CHAPTER 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Installing Pyspark and Setting up Your Development Environment

In this chapter, we are going to introduce Spark and learn the core concepts, such as, SparkContext, and Spark tools such as SparkConf and Spark shell. The only prerequisite is the knowledge of basic Python concepts and the desire to seek insight from big data. We will learn how to analyze and discover patterns with Spark SQL to improve our business intelligence. Also, you will be able to quickly iterate through your solution by setting to PySpark for your own computer. By the end of the book, you will be able to work with real-life messy data sets using PySpark to get practical big data experience.

In this chapter, we will cover the following topics:

  • An overview of PySpark
  • Setting up Spark on Windows and PySpark
  • Core concepts in Spark and PySpark
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