Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David 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 (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Practical applications


Now that we have our algorithm coded, let's look at practical applications for this method on real data. We will start by understanding how the algorithm performs, so that we can determine where we might use it.

Algorithm characteristics

So, what are the characteristics of this algorithm? Below is a list of strengths and weaknesses.

Advantages

The advantages are as follows:

  • The algorithm is general, lending itself well to both stream based and Spark implementations

  • The theory is simple, yet effective

  • The implementation is fast and efficient

  • The result is visual and interpretable

  • The method is stackable and allows for multi scale studies; this is very simple when using Spark windows

Disadvantages

The disadvantages are as follows:

  • A lagging indicator the algorithm finds trend reversals that occurred in the past, and cannot be used directly to predict a trend change as it happens

  • The lag accumulates for higher scales, meaning much more data (and thus time lag) is required to find...