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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 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

Parallelization with Spark RDDs

Now that we know how to create RDDs within the text file that we received from the internet, we can look at a different way to create this RDD. Let's discuss parallelization with our Spark RDDs.

In this section, we will cover the following topics:

  • What is parallelization?
  • How do we parallelize Spark RDDs?

Let's start with parallelization.

What is parallelization?

The best way to understand Spark, or any language, is to look at the documentation. If we look at Spark's documentation, it clearly states that, for the textFile function that we used last time, it reads the text file from HDFS.

On the other hand, if we look at the definition of parallelize, we can see that this is...

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