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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 discussed how Spark works in a cluster mode with its underlying architecture. You also saw how to deploy a full Spark application on a cluster. You saw how to deploy cluster for running Spark application in different cluster modes such as local, standalone, YARN, and Mesos. Finally, you saw how to configure Spark cluster on AWS using EC2 script. We believe that this chapter will help you to gain some good understanding of Spark. Nevertheless, due to page limitation, we could not cover many APIs and their underlying functionalities.

If you face any issues, please don't forget to report this to Spark user mailing list at user@spark.apache.org. Before doing so, make sure that you have subscribed to it. In the next chapter, you will see how to test and debug Spark applications.

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