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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 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

Loading and saving data

Loading data into an RDD and Saving an RDD onto an output system both support several different methods. We will cover the most common ones in this section.

Loading data

Loading data into an RDD can be done by using SparkContext. Some of the most common methods are:.

  • textFile
  • wholeTextFiles
  • load from a JDBC datasource

textFile

textFile() can be used to load textFiles into an RDD and each line becomes an element in the RDD.

sc.textFile(name, minPartitions=None, use_unicode=True)

The following is an example of loading a textfile into an RDD using...

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