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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
Author Profile Icon James Cross
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 FREE CHAPTER 2. Getting Your Big Data into the Spark Environment Using RDDs 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

Loading data on to Spark RDDs

In this section, we are going to look at loading data on to Spark RDDs, and will cover the following topics:

  • The UCI machine learning data repository
  • Getting data from the repository to Python
  • Getting data into Spark

Let's start with an overview of the UCI machine learning data repository.

The UCI machine learning repository

We can access the UCI machine learning repository by navigating to https://archive.ics.uci.edu/ml/. So, what is the UCI machine learning repository? UCI stands for the University of California Irvine machine learning repository, and it is a very useful resource for getting open source and free datasets for machine learning. Although PySpark's main issue or solution...

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