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Scala and Spark for Big Data Analytics

You're reading from   Scala and Spark for Big Data Analytics Explore the concepts of functional programming, data streaming, and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Length 796 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Toc

Table of Contents (19) Chapters Close

Preface 1. Introduction to Scala 2. Object-Oriented Scala FREE CHAPTER 3. Functional Programming Concepts 4. Collection APIs 5. Tackle Big Data – Spark Comes to the Party 6. Start Working with Spark – REPL and RDDs 7. Special RDD Operations 8. Introduce a Little Structure - Spark SQL 9. Stream Me Up, Scotty - Spark Streaming 10. Everything is Connected - GraphX 11. Learning Machine Learning - Spark MLlib and Spark ML 12. My Name is Bayes, Naive Bayes 13. Time to Put Some Order - Cluster Your Data with Spark MLlib 14. Text Analytics Using Spark ML 15. Spark Tuning 16. Time to Go to ClusterLand - Deploying Spark on a Cluster 17. Testing and Debugging Spark 18. PySpark and SparkR

Everything is Connected - GraphX

"Technology made large populations possible; large populations now make technology indispensable."

- Joseph Wood Krutch

In this chapter, we'll learn how many real-world problems can be modeled (and resolved) using graphs. We see that Apache Spark comes with its own graph library, and what you learned about RDDs can be used here too (this time as vertex and edge RDDs).
In a nutshell, the following topics will be covered in this chapter:

  • A brief introduction to graph theory
  • GraphX
  • VertexRDD and EdgeRDD
  • Graph operators
  • Pregel API
  • PageRank
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