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 introduced the idea of data architecture and explained how to group responsibilities into capabilities that help manage data throughout its lifecycle. We explained that all data handling requires a level of due diligence, whether this is enforced by corporate rules or otherwise, and without this, analytics and their results can quickly become invalid.

Having scoped our data architecture, we have walked through the individual components and their respective advantages/disadvantages, explaining that our choices are based upon collective experience. Indeed, there are always options when it comes to choosing components and their individual features should always be carefully considered before any commitment.

In the next chapter, we will dive deeper into how to source and capture data. We will advise on how to bring data onto the platform and discuss aspects related to processing and handling data through a pipeline.