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

Loading your data


As we have outlined in previous chapters, traditional system engineering commonly adopts a pattern to move the data from its source to its destination, that is, ETL, whereas Spark tends to rely on schema-on-read. As it's important to understand how these concepts relate to schemas and input formats, let's describe this aspect in more detail:

On the face of it, the ETL approach seems to be sensible, and indeed has been implemented by just about every organization that stores and handles data. There are some very popular, feature-rich products out there that perform the ETL task very well - not to mention Apache's open source offering, Apache Camel http://camel.apache.org/etl-example.html.

However, this apparently straightforward approach belies the true effort required to implement even a simple data pipeline. This is because we must ensure that all data complies with a fixed schema before we can use it. For example, if we wanted to ingest some data from a starting directory...