Book Image

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
Book Image

Mastering Spark for Data Science

By: Bifet, Morgan, Amend, Hallett, 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 (15 chapters)

Summary

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...