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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

Customer Feedback

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

Design considerations for building scalable stream processing applications

Building robust stream processing applications is challenging. The typical associated with stream processing include the following:

Complex Data: Diverse data formats and the of data create significant challenges streaming applications. Typically, the data is available in various formats, such as JSON, CSV, AVRO, and binary. Additionally, dirty data, or late arriving, and out-of-order data, can make the design of such applications extremely complex.
Complex workloads: Streaming applications to support a diverse set of application requirements, including interactive queries, machine learning pipelines, and so on.
Complex systems: With diverse systems, including Kafka, S3, Kinesis, and so on, system failures can lead to significant reprocessing or bad results.

Steam processing using Spark SQL can be fast, scalable, and fault-tolerant. It provides an extensive set of high-level APIs to deal with complex data and workloads...