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Learning Spark SQL

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

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785888359
Length 452 pages
Edition 1st Edition
Languages
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Author (1):
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Aurobindo Sarkar Aurobindo Sarkar
Author Profile Icon Aurobindo Sarkar
Aurobindo Sarkar
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Table of Contents (13) Chapters Close

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

Building Spark streaming applications

In this section, we will primarily focus on the newly introduced structured streaming feature (in Spark 2.0). Structured streaming APIs are GA with Spark 2.2 and using them is the preferred method for building streaming Spark applications. Several updates to Kafka-based processing components including performance improvements have also been released in Spark 2.2. We introduced structured streaming in Chapter 1, Getting Started with Spark SQL. In this chapter, we will get deeper into the topic and present several code examples to showcase its capabilities.

As a quick recap, structured streaming provides a fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the developer having to reason about the underlying streaming mechanisms.

It is built on the Spark SQL engine, and the streaming computations can be expressed...

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