Book Image

Scala Data Analysis Cookbook

By : Arun Manivannan
Book Image

Scala Data Analysis Cookbook

By: Arun Manivannan

Overview of this book

This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you’ll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.
Table of Contents (14 chapters)
Scala Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


In all honesty, free / open source data visualization tools in Scala aren't that rich compared to those in other mature data analysis languages, such as R or Python. We might partly attribute this to the lack of rich charting frameworks in Java, and visualization has never been a strong point for big data analytics.

That said, Scala (or more specifically the Hadoop world, including Spark) is catching up with the presence of the Apache incubator project Zeppelin and the highly active Scala bindings (https://github.com/bokeh/bokeh-scala) for the Bokeh project (http://bokeh.pydata.org/en/latest/). With R becoming the first-class citizen in Spark—with the availability of SparkR DataFrames from 1.4 onwards—Spark gets additional visualization from R other than the already existing Python APIs.

As a side note, all existing Java libraries are accessible from Scala. Hence, we are free to borrow any visualization library from Java.