The remaining part of our application is to start classifying data. As introduced earlier, the reason for using Twitter was to steal ground truth from external resources. We will train a Naive Bayes classification model using Twitter data while predicting categories of the GDELT URLs. The convenient side of using a Kappa architecture approach is that we do not have to worry much about exporting some common pieces of code across different applications or different environments. Even better, we do not have to export/import our model between a batch and a speed layer (both GDELT and Twitter, sharing the same Spark context, are part of the same physical layer). We could save our model to HDFS for auditing purposes, but we simply need to pass a reference to a Scala object between both classes.
Mastering Spark for Data Science
By :
Mastering Spark for Data Science
By:
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
Free Chapter
The Big Data Science Ecosystem
Data Acquisition
Input Formats and Schema
Exploratory Data Analysis
Spark for Geographic Analysis
Scraping Link-Based External Data
Building Communities
Building a Recommendation System
News Dictionary and Real-Time Tagging System
Story De-duplication and Mutation
Anomaly Detection on Sentiment Analysis
TrendCalculus
Secure Data
Customer Reviews