Book Image

Scala for Data Science

By : Pascal Bugnion
Book Image

Scala for Data Science

By: Pascal Bugnion

Overview of this book

Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala’s emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions.
Table of Contents (22 chapters)
Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Building and running standalone programs


So far, we have interacted exclusively with Spark through the Spark shell. In the section that follows, we will build a standalone application and launch a Spark program either locally or on an EC2 cluster.

Running Spark applications locally

The first step is to write the build.sbt file, as you would if you were running a standard Scala script. The Spark binary that we downloaded needs to be run against Scala 2.10 (You need to compile Spark from source to run against Scala 2.11. This is not difficult to do, just follow the instructions on http://spark.apache.org/docs/latest/building-spark.html#building-for-scala-211).

// build.sbt file

name := "spam_mi"

scalaVersion := "2.10.5"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "1.4.1"
)

We then run sbt package to compile and build a jar of our program. The jar will be built in target/scala-2.10/, and called spam_mi_2.10-0.1-SNAPSHOT.jar. You can try this with the example code provided...