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You're reading from Scala and Spark for Big Data Analytics Explore the concepts of functional programming, data streaming, and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785280849

Length 796 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Karim

Sridhar Alla

View More author details

Table of Contents (25) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

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

Why FP and Scala for learning Spark?

In this section, we will discuss why we will learn Spark to our data analytics problem. We will then discuss why the functional programming concepts in Scala are particularly important to make data analysis easier for the data scientists. We will also discuss the Spark programming and its ecosystem to make them clearer.

Why Spark?

s a lightning fast cluster computing framework and is mainly designed for fast computations. Spark is based on the Hadoop MapReduce model and uses MapReduce in more forms and types of computation, such as interactive queries and stream processing. One of the main features of Spark is in-memory processing, which helps increase the performance and processing speed of an application. Spark supports a wide range of applications and workloads, such as the following: