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Learning Spark SQL

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):

Aurobindo Sarkar

View More author details

Table of Contents (13) Chapters

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

Using Spark SQL in Graph Applications

In this chapter, we will present typical use cases for using Spark SQL in graph applications. Graphs are common in many different domains. Typically, graphs are analyzed using special graph processing engines. GraphX is the Spark component for graph computations. It is based on RDDs and supports graph abstractions and operations, such as subgraphs, aggregateMessages, and so on. In addition, it also exposes a variant of the Pregel API. However, our focus will be on the GraphFrame API implemented on top of Spark SQL Dataset/DataFrame APIs. GraphFrames is an integrated system that combines graph algorithms, pattern matching, and queries. GraphFrame API is still in beta (as of Spark 2.2) but is definitely the future graph processing API for Spark applications.

More specifically, in this chapter, you will learn the following topics:

Using GraphFrames...

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Authors (1)

Sarkar

Sarkar

Aurobindo Sarkar leads a team of data scientists and engineers at Session AI, developing cloud-based ML models for in-session marketing in e-commerce and retail. As a former CTO at multiple SaaS startups, he has architected secure, scalable, and highly available AWS cloud applications. His research interests now focus on AWS-based large-scale transformer models for NLP and HFT models for the futures and options market. Aurobindo holds a bachelor's degree in engineering from IIT Delhi, a master's in management from the Indian Institute of Science Bangalore, and a master's in computer science from New York University.

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