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You're reading from Learning Spark SQL Architect streaming analytics and machine learning solutions

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781785888359

Length 452 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Streaming

Author (1):

Sarkar

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Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

1. Getting Started with Spark SQL FREE CHAPTER

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

Understanding DataFrame/Dataset APIs

A Dataset is a strongly collection of domain-specific objects that can be transformed parallelly, using functional or relational operations. Each Dataset also a view called a DataFrame, which is not strongly typed and is essentially a Dataset of row objects.

Spark SQL applies structured views to the data from different source systems stored using different data formats. Structured APIs, such as the DataFrame/Dataset API, allows developers to use a high-level API to write their programs. These APIs allow them to focus on the "what" rather than the "how" of the data processing required.

Even though applying a structure can limit what can be expressed, in practice, structured APIs can accommodate the vast majority of computations required in application development. Also, it is these very limitations (imposed by structured APIs) that present several of the main optimization opportunities.

In the next section, we will explore encoders and their role in efficient...