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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. A First Taste and What’s New in Apache Spark V2

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

Chapter 2. Apache Spark SQL

In this chapter, we will examine ApacheSparkSQL, SQL, DataFrames, and Datasets on top of Resilient Distributed Datasets (RDDs). DataFrames were introduced in Spark 1.3, basically replacing SchemaRDDs, and are columnar data storage structures roughly equivalent to relational database tables, whereas Datasets were introduced as experimental in Spark 1.6 and have become an additional component in Spark 2.0.

We have tried to reduce the dependency between individual chapters as much as possible in order to give you the opportunity to work through them as you like. However, we do recommend that you read this chapter because the other chapters are dependent on the knowledge of DataFrames and Datasets.

This chapter will cover the following topics:

SparkSession
Importing and saving data
Processing the text files
Processing the JSON files
Processing the Parquet files
DataSource API
DataFrames
Datasets
Using SQL
User-defined functions
RDDs versus DataFrames versus Datasets

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