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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
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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 FREE CHAPTER 2. Spark Basics and Spark Examples 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

The Resilient Distributed Dataset (RDD)

In this section, we'll stop being all high level and hand-wavy and go into a little bit more depth about how Spark works from a technical standpoint. In Spark, under the hood, there's something called the Resilient Distributed Dataset object, which is like a core object that everything in Spark revolves around. Even for the libraries built on top of Spark, such as Spark SQL or MLlib, you're also using RDDs under the hood or extensions to the RDD objects to make it look like something a little bit more structured. If you understand what an RDD is in Spark, you've come ninety per cent of the way to understanding Spark.

What is the RDD?

Let's talk about the RDD...

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