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You're reading from Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787126497

Length 350 pages

Edition 1st Edition

Languages

Java

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Sourav Gulati

Sumit Kumar

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Table of Contents (12) Chapters

Preface

1. Introduction to Spark FREE CHAPTER

2. Revisiting Java

3. Let Us Spark

4. Understanding the Spark Programming Model

5. Working with Data and Storage

6. Spark on Cluster

7. Spark Programming Model - Advanced

8. Working with Spark SQL

9. Near Real-Time Processing with Spark Streaming

10. Machine Learning Analytics with Spark MLlib

11. Learning Spark GraphX

Graph algorithms

The Spark Graphx library provides built-in implementations for some very popular graph algorithms. These implementations help to perform various graph-based analytics in a simplified manner. The org.apache.Spark.graphx.GraphOps API allows for executing these operations on the graph. In this section, we will run graph-based analytics using the following implementations:

PageRank

PageRank is one of the most popular algorithms in graph theory. It is used to rank the vertices based on their importance. The importance of a vertex is calculated by the number of edges directed to the vertex. For example, a user is highly ranked on Twitter based on their followers, that is, the number of directed edges to that user...

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Authors (2)

Gulati

Shekhar Gulati is a developer and OpenShift evangelist working with Red Hat. He has been evangelizing about OpenShift for the last 2 years. He regularly speaks at various conferences and user groups around the world to spread the goodness of OpenShift. He regularly blogs on the OpenShift official blog and has written more than 50 blogs on OpenShift. Shekhar has also written many technical articles for IBM developerWorks, Developer.com, and Javalobby.

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Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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