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

Our Twitter mechanical Turk


The accuracy of a classification algorithm should be measured against a test dataset, meaning a labeled dataset that was not included in the training phase. We do not have access to such a dataset (this is the reason we bootstrapped our model initially), hence we cannot compare the original versus predicted categories. Instead of the true accuracy, we can estimate an overall confidence level by visualizing our results. With all our data on Elasticsearch, we build a Kibana dashboard with an additional plugin for tag cloud visualizations (https://github.com/stormpython/tagcloud).

The following figure shows the number of GDELT articles that were analyzed and predicted on May 1, 2016. Around 18,000 articles have been downloaded in less than 24h (by batch interval of 15 minutes). At each batch, we observe no more than 100 distinct predicted hashtags; this is fortunate as we only kept the top 100 popular hashtags occurring within a 24h time window. Besides, it gives...