Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Scala and Spark for Big Data Analytics

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

Arrow left icon
Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Length 796 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Authors (2):
Arrow left icon
Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

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

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the to the BeautifulSoup function."

A block of code is set as follows:

package com.chapter11.SparkMachineLearning
import org.apache.spark.mllib.feature.StandardScalerModel
import org.apache.spark.mllib.linalg.{ Vector, Vectors }
import org.apache.spark.sql.{ DataFrame }
import org.apache.spark.sql.SparkSession

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

val spark = SparkSession
.builder
.master("local[*]")
.config("spark.sql.warehouse.dir", "E:/Exp/")
.config("spark.kryoserializer.buffer.max", "1024m")
.appName("OneVsRestExample")
.getOrCreate()

Any command-line input or output is written as follows:

$./bin/spark-submit --class com.chapter11.RandomForestDemo \
--master spark://ip-172-31-21-153.us-west-2.compute:7077 \
--executor-memory 2G \
--total-executor-cores 2 \
file:///home/KMeans-0.0.1-SNAPSHOT.jar \
file:///home/mnist.bz2

New termsand important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."

Warnings or important notes appear like this.
Tips and tricks appear like this.
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image