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


In this chapter, we have concluded our journey by discussing aspects of distributed computing performance, and what to exploit when writing your own scalable analytics. Hopefully, you've come away with a sense of some of the challenges involved, and have a better understanding of how Spark works under the covers.

Apache Spark is a constantly evolving framework and new features and improvements are being added every day. No doubt it will become increasingly easier to use as continuous tweaks and refinements are intelligently applied into the framework, automating much of what must be done manually today.

In terms of what's next, who knows what's round the corner? But with Spark beating the competition yet again to win the 2016 CloudSort Benchmark ( and new versions set to be released every four months, one thing is for sure, it's going to be fast-paced. And hopefully, with the solid principles and methodical guidelines that you've learned in this chapter, you...