Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David George

Overview of this book

Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (22 chapters)
Mastering Spark for Data Science
About the Authors
About the Reviewer
Customer Feedback


Now that we have a general plan of action, before exploring our data, we must first invest in building the reusable tools for conducting the early mundane parts of the exploration pipeline that help us validate data; then as a second step investigate GDELT's content.

Introducing mask based data profiling

A simple but effective method for quickly exploring new types of data is to make use of mask based data profiling. A mask in this context is a transformation function for a string that generalizes a data item into a feature, that, as a collection of masks, will have a lower cardinality than the original values in the field of study.

When a column of data is summarized into mask frequency counts, a process commonly called data profiling, it can offer rapid insights into the common structures and content of the strings, and hence reveal how the raw data was encoded. Consider the following mask for exploring data:

  • Translate uppercase letters to A

  • Translate lowercase letters to a

  • Translate...