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Scala and Spark for Big Data Analytics

You're reading from  Scala and Spark for Big Data Analytics

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Pages 796 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla
View More author details

Table of Contents (19) Chapters

Preface Introduction to Scala Object-Oriented Scala Functional Programming Concepts Collection APIs Tackle Big Data – Spark Comes to the Party Start Working with Spark – REPL and RDDs Special RDD Operations Introduce a Little Structure - Spark SQL Stream Me Up, Scotty - Spark Streaming Everything is Connected - GraphX Learning Machine Learning - Spark MLlib and Spark ML My Name is Bayes, Naive Bayes Time to Put Some Order - Cluster Your Data with Spark MLlib Text Analytics Using Spark ML Spark Tuning Time to Go to ClusterLand - Deploying Spark on a Cluster Testing and Debugging Spark PySpark and SparkR

Summary

In this chapter, we have explored some functional programming concepts in Scala. We have seen what functional programming is and how Scala supports it, why it matters, and the advantages of using functional concepts. We have seen why learning FP concepts is important in learning the Spark paradigm. Pure functions, anonymous functions, and higher-order functions were discussed with suitable examples. Later in this chapter, we saw how to handle exceptions in the higher-order functions outside collections using the standard library of Scala. Finally, we discussed how functional Scala affects object mutability.

In the next chapter, we will provide an in-depth analysis on the Collections API, one of the most prominent features of the standard library.

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