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
About the Author
About the Reviewers

Storing data as Parquet files

Parquet ( is rapidly becoming the go-to data storage format in the world of big data because of the distinct advantages it offers:

  • It has a column-based representation of data. This is better represented in a picture, as follows:

    As you can see in the preceding screenshot, Parquet stores data in chunks of rows, say 100 rows. In Parquet terms, these are called RowGroups. Each of these RowGroups has chunks of columns inside them (or column chunks). Column chunks can hold more than a single unit of data for a particular column (as represented in the blue box in the first column). For example. Jai, Suri, and Dhina form a single chunk even though they are composed of three single units of data for Name.

    Another unique feature is that these column chunks (groups of a single column's information) can be read independently. Let's consider the following image:

    We can see that the items of column data are stored next to each other in a sequence...