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

Uninformed data


The following technique could be seen as something of a game changer in how most modern data scientists work. While it is common to work with structured and unstructured text, it is less common to work on raw binary data the reason being the gap between computer science and data science. Textual processing is limited to a standard set of operations that most will be familiar with, that is, acquiring, parsing and storing, and so on. Instead of restricting ourselves to these operations, we will work directly with audio transforming and enrich the uninformed signal data into informed transcription. In doing this, we enable a new type of data pipeline that is analogous to teaching a computer to hear the voice from audio files.

A second (breakthrough) idea that we encourage here is a shift in thinking around how data scientists engage with Hadoop and big data nowadays. While many still consider these technologies as just yet another database, we want to showcase the vast array...