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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

GraphX

As shown in the preceding section, we can model many real-life use cases as Graphs with a set of vertices and a set of edges linking the vertices. We also wrote simple code trying to implement some basic graph operations and queries such as, Is X a friend of Y ? However, as we explored further, the algorithms only get more complicated along with use cases and also the size of graphs is much much larger than can be handled on one machine.

It is not possible to fit one billion Facebook users along with all their friendship relations into one machine or even a few machines.

What we need to do is to look beyond the one machine and few machines thrown together and rather start considering highly scalable architectures to implement the complex graph algorithms, which can handle the volume of data and complex interconnections of the data elements. We have already seen an introduction...

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