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

Processing Twitter data


The second main constraint of using Twitter is the constraint of noise. When most classification models are trained against dozens of different classes, we will be working against hundreds of thousands of distinct hashtags per day. We will be focusing on popular topics only, meaning the trending topics occurring within a defined batch window. However, because a 15 minute batch size on Twitter will not be sufficient enough to detect trends, we will apply a 24-hour moving window where all hashtags will be observed and counted, and where only the most popular ones will be kept.

Figure 9: Twitter online layer, batch and window size

Using this approach, we reduce the noise of unpopular hashtags, making our classifier much more accurate and scalable, and significantly reducing the number of articles to fetch as we only focus on trending URLs mentioned alongside popular topics. This allows us to save lots of time and resources spent analyzing irrelevant data (with regards...