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

Interacting with data sources


A major challenge in data science or engineering is dealing with the wealth of input and output formats for persisting data. We might receive or send data as CSV files, JSON files, or through a SQL database, to name a few.

Spark provides a unified API for serializing and de-serializing DataFrames to and from different data sources.

JSON files

Spark supports loading data from JSON files, provided that each line in the JSON file corresponds to a single JSON object. Each object will be mapped to a DataFrame row. JSON arrays are mapped to arrays, and embedded objects are mapped to structs.

This section would be a little dry without some data, so let's generate some from the GitHub API. Unfortunately, the GitHub API does not return JSON formatted as a single object per line. The code repository for this chapter contains a script, FetchData.scala which will download and format JSON entries for Martin Odersky's repositories, saving the objects to a file named odersky_repos...