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Learning Spark SQL

You're reading from Learning Spark SQL

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781785888359

Pages 452 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Aurobindo Sarkar

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Spark SQL

2. Using Spark SQL for Processing Structured and Semistructured Data

3. Using Spark SQL for Data Exploration

4. Using Spark SQL for Data Munging

5. Using Spark SQL in Streaming Applications

6. Using Spark SQL in Machine Learning Applications

7. Using Spark SQL in Graph Applications

8. Using Spark SQL with SparkR

9. Developing Applications with Spark SQL

10. Using Spark SQL in Deep Learning Applications

11. Tuning Spark SQL Components for Performance

12. Spark SQL in Large-Scale Application Architectures

Chapter 2. Using Spark SQL for Processing Structured and Semistructured Data

In this chapter, we will familiarize you with using Spark SQL with different types of data sources and data storage formats. Spark provides easy and standard structures (that is, RDDs and DataFrames/Datasets) to work with both structured and semistructured data. We include some of the data sources that are most commonly used in big data applications, such as, relational data, NoSQL databases, and files (CSV, JSON, Parquet, and Avro). Spark also allows you to define and use custom data sources. A series of hands-on exercises in this chapter will enable you to use Spark with different types of data sources and data formats.

In this chapter, you shall learn the following topics:

Understanding data sources in Spark applications
Using JDBC to work with relational databases
Using Spark with MongoDB (NoSQL database)
Working with JSON data
Using Spark with Avro and Parquet Datasets

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Authors (1)

Aurobindo Sarkar

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