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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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The TrendCalculus algorithm

In this section we will explain the detail of the TrendCalculus implementation, using the Brent oil price data set seen in Chapter 5, Spark for Geographic Analysis, as an example use case.

Trend windows

In order to measure any type of change, we must first quantify it in some way. For trends, we are going to define this in the following manner:

  • Overall positive change (usually expressed as a value increase)

Higher highs and higher lows => +1

  • Overall negative change (usually expressed as a value decrease)

Lower highs and lower lows => -1

We must therefore translate our data into a time series of trend direction, being either +1 or -1. By splitting our data into a series of windows, size n, we can calculate the dated highs and lows for each of them:

Trend windows

Since this type of windowing is a common practice in data science, it is reasonable to think there must be an implementation in Spark; if you have read Chapter 5, Spark for Geographic Analysis you will have seen them...

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