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

Chapter 3. Input Formats and Schema

The aim of this chapter is to demonstrate how to load data from its raw format onto different schemas, therefore enabling a variety of different kinds of downstream analytics to be run over the same data. When writing analytics, or even better, building libraries of reusable software, you generally have to work with interfaces of fixed input types. Therefore, having flexibility in how you transition data between schemas, depending on the purpose, can deliver considerable downstream value, both in terms of widening the type of analysis possible and the re-use of existing code.

Our primary objective is to learn about the data format features that accompany Spark, although we will also delve into the finer points of data management by introducing proven methods that will enhance your data handling and increase your productivity. After all, it is most likely that you will be required to formalize your work at some point, and an introduction to how to avoid...