In the previous section, we were covering an interesting use case, how to extract location entities from unstructured data. In this section, we will make our enrichment process even smarter by trying to retrieve the actual geographical coordinate information (such as latitude and longitude) based on the locations of entities we were able to identify. Given an input string London, can we detect the city of London - UK together with its relative latitude and longitude? We will be discussing how to build an efficient geo lookup system that does not rely on any external API and which can process location data of any scale by leveraging the Spark framework and the Reduce-Side-Join pattern. When building this lookup service, we will have to bear in mind many places around the world might be sharing the same name (there are around 50 different places called Manchester in the US alone), and that an input record may not use the official name of the place it would be referring to (the official...
Mastering Spark for Data Science
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Mastering Spark for Data Science
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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.
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Current Title:
Mastering Spark for Data Science
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