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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from   Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Length 296 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Spark 2. Spark Basics and Spark Examples FREE CHAPTER 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Key/value RDDs and the average friends by age example


A powerful thing to do with RDDs is to put more structured data into it. One thing we can do is put key/value pairs of information into Spark RDDs and then we can treat it like a very simple database, if you will. So let's walk through an example where we have a fabricated social network set of data, and we'll analyze that data to figure out the average number of friends, broken down by age of people in this fake social network. We'll use key/value pairs and RDDs to do that. Let's cover the concepts, and then we'll come back later and actually run the code.

Key/value concepts - RDDs can hold key/value pairs

RDDs can hold key/value pairs in addition to just single values. In our previous examples, we looked at RDDs that included lines of text for an input data file or that contained movie ratings. In those cases, every element of the RDD contained a single value, either a line of text or a movie rating, but you can also store more structured...

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Frank Kane's Taming Big Data with Apache Spark and Python
Published in: Jun 2017
Publisher: Packt
ISBN-13: 9781787287945
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