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
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


Data science is not just about machine learning. In fact, machine learning is only a small portion of it. In our understanding of what modern data science is, the science often happens exactly here, at the data enrichment process. The real magic occurs when one can transform a meaningless dataset into a valuable set of information and get new insights out of it. In this section, we have been describing how to build a fully functional data insight system using nothing more than a simple collection of URLs (and a bit of elbow grease).

In this chapter, we demonstrated how to create an efficient web scraper with Spark using the Goose library and how to extract and de-duplicate features out of raw text using NLP techniques and the GeoNames database. We also covered some interesting design patterns such as mapPartitions and Bloom filters that will be discussed further in Chapter 14, Scalable Algorithms.

In the next chapter, we will be focusing on the people we were able to extract from all...