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


In this chapter, we have seen why datasets should always be thoroughly understood before too much exploration work is undertaken. We have discussed the details of structured data and dimensional modeling, particularly with respect to how this applies to the GDELT dataset, and have expanded the GKG model to show its underlying complexity.

We have explained the difference between the traditional ETL and newer schema-on-read ELT techniques, and have touched upon some of the issues that data engineers face regarding data storage, compression, and data formats - specifically the advantages and implementations of Avro and Parquet. We have also demonstrated that there are several ways to explore data using the various Spark API, including examples of how to use SQL on the Spark shell.

We can conclude this chapter by mentioning that the code in our repository pulls everything together and is a full model for reading in raw GKG files (use the Apache NiFi GDELT data ingest pipeline from Chapter...