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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

Summary

In this chapter, we showed some examples of how to write your Spark code in Python and R. These are the most popular programming languages in the data scientist community.

We covered the motivation of using PySpark and SparkR for big data analytics with almost similar ease with Java and Scala. We discussed how to install these APIs on their popular IDEs such as PyCharm for PySpark and RStudio for SparkR. We also showed how to work with DataFrames and RDDs from these IDEs. Furthermore, we discussed how to execute Spark SQL queries from PySpark and SparkR. Then we also discussed how to perform some analytics with visualization of the dataset. Finally, we saw how to use UDFs with PySpark with examples.

Thus, we have discussed several aspects for two Spark's APIs; PySpark and SparkR. There are much more to explore. Interested readers should refer to their websites for...

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