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  • Book Overview & Buying Mastering Spark for Data Science
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Mastering Spark for Data Science

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
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Mastering Spark for Data Science

Mastering Spark for Data Science

4 (2)
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)
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Chapter 12. TrendCalculus

Long before the concept of what's trending became a popular topic of study by data scientists, there was an older one that is still not well served by data science: it is that of Trends. Presently, the analysis of trends, if it can be called that, is primarily carried out by people "eyeballing" time series charts and offering interpretations. But what is it that people's eyes are doing?

This chapter describes an implementation in Apache Spark of a new algorithm for studying trends numerically, called TrendCalculus, invented by Andrew Morgan. The original reference implementation is written in the Lua language and was open-sourced in 2015, the code can be viewed at https://bitbucket.org/bytesumo/trendcalculus-public.

This chapter explains the core method, which delivers the fast extraction of trend change points on a time series; these are the moments when trends change direction. We will describe our TrendCalculus algorithm in detail...

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Mastering Spark for Data Science
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