In this chapter, we have introduced a method for analyzing trends with TrendCalculus. We have outlined the fact that despite analysis of trends being a very common use case, there are few tools to aid the data scientist in this cause apart from very general-purpose visualization software. We have guided the reader through the TrendCalculus algorithm, demonstrating how we implement an efficient and scalable realization of the theory in Spark. We have described the process of identifying the key output of the algorithm: trend reversals on a named scale. Having calculated reversals, we used D3.js to visualize time series data that has been summarized for one-week windows, and plotted trend reversals. The chapter continued with an explanation of how to overcome the main edge case: the zero values found during simple trend calculation. We have concluded with a brief outline of the algorithm characteristics and potential use cases, demonstrating how the method is elegant and can be easily...
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