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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

Using Spark SQL for basic data analysis

Interactively, and visualizing large data is as the queries can take a long time to execute and the visual interface cannot accommodate as many pixels as data points. Spark supports in-memory computations and a high degree of parallelism to achieve interactivity with large distributed data. In addition, Spark is capable of handling petabytes of data and provides a set of versatile programming interfaces and libraries. These include SQL, Scala, Python, Java and R APIs, and libraries for distributed statistics and machine learning.

For data that fits into a single computer, there are many good tools available, such as R, MATLAB, and others. However, if the data does not fit into a single machine, or if it is very complicated to get the data to that machine, or if a single computer cannot easily process the data, then this section will offer some good tools and techniques for data exploration.

In this section, we will go through some basic data exploration...

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Authors (1)

Aurobindo Sarkar

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