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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
Toc

Table of Contents (19) Chapters close

Preface 1. Introduction to Scala 2. Object-Oriented Scala 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

Functional programming and data mutability

Pure functional programming is one of the best practices in functional programming and you should stick to it. Writing pure functions will make your programming life easier and you will be able to write code that's easy to maintain and extend. Also, if you want to parallelize your code then it will be easier to do so if you write pure functions.

If you're an FP purist, one drawback of using functional programming in Scala is that Scala supports both OOP and FP (see Figure 1), and therefore it's possible to mix the two coding styles in the same code base. In this chapter, we have seen several examples showing that writing pure functions is easy. However, combining them into a complete application is difficult. You might agree that advanced topics such as monads make FP intimidating.

I talked to many people and they think...

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