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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
Author (1):
Aurobindo Sarkar 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

Summary


In this chapter, we introduced deep learning in Spark. We discussed various types of deep neural networks and their application. We also explored a few code examples provided in the BigDL distribution. As this is a rapidly evolving area in Spark, presently, we expect these libraries to provide a lot more functionalities using Spark SQL and the DataFrame/Dataset APIs. Additionally, we also expect them to mature and become more stable over the coming months.

In the next chapter, we will shift our focus to tuning Spark SQL applications. We will cover key foundational aspects regarding serialization/deserialization using encoders and the logical and physical plans associated with query executions, and then present the details of the cost-based optimization (CBO) feature released in Spark 2.2. Additionally, we will present some tips and tricks that developers can use to improve the performance of their applications.

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