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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 FREE CHAPTER 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

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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