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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

Deploying Spark machine learning pipelines


The following figure illustrates a learning pipeline at a conceptual level. However, real-life ML pipelines are a lot more complicated, with several models being trained, tuned, combined, and so on:

The next figure shows the core elements of a typical machine learning application split into two parts: the modeling, including model training, and the deployed model (used on streaming data to output the results):

Typically, data scientists experiment or do their modeling work in Python and/or R. Their work is then reimplemented in Java/Scala before deployment in a production environment. Enterprise production environments often consist of web servers, application servers, databases, middleware, and so on. The conversion of prototypical models to production-ready models results in additional design and development effort that lead to delays in rolling out updated models.

We can use Spark MLlib 2.x model serialization to directly use the models and pipelines...

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