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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 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

SparkSQL, DataFrames, and DataSets

In this chapter, we'll spend some time talking about SparkSQL. This is becoming an increasingly important part of Spark; it basically lets you deal with structured data formats. This means that instead of the RDDs that contain arbitrary information in every row, we're going to give the rows some structure. This will let us do a lot of different things, such as treat our RDDs as little databases. So, we're going to call them DataFrames and DataSets from now on, and you can actually perform SQL queries and SQL-like operations on them, which can be pretty powerful.

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