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

Client-side program architecture


The basic idea is simple: the user searches for the name of someone on GitHub in the input box. When he enters a name, we fire a request to the API designed earlier in this chapter. When the response from the API returns, the program binds that response to a model and emits an event notifying that the model has been changed. The views listen for this event and refresh from the model in response.

Designing the model

Let's start by defining the client-side model. The model holds information regarding the repos of the user currently displayed. It gets filled in after the first search.

// public/javascripts/model.js

define([], function(){
   return {
    ghubUser: "", // last name that was searched for
    exists: true, // does that person exist on github?
    repos: [] // list of repos
  } ;
});

To see a populated value of the model, head to the complete application example on app.scala4datascience.com, open a JavaScript console in your browser, search for a user...