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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

Basics of RDD operation

Let's now go through some RDD operational basics. The best way to understand what something does is to look at the documentation so that we can get a rigorous understanding of what a function performs.

The reason why this is very important is that the documentation is the golden source of how a function is defined and what it is designed to be used as. By reading the documentation, we make sure that we are as close to the source as possible in our understanding. The link to the relevant documentation is https://spark.apache.org/docs/latest/rdd-programming-guide.html.

So, let's start with the map function. The map function returns an RDD by applying the f function to each element of this RDD. In other words, it works the same as the map function we see in Python. On the other hand, the filter function returns a new RDD containing only the elements...

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