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
About the Authors
About the Reviewer
Customer Feedback

Plotting your course

It's easy to overlook planning and preparation when you're preoccupied with experimenting on the latest technologies and data! Nevertheless, the process of how you write scalable algorithms is just as important as the algorithms themselves. Therefore, it's crucial to understand the role of planning in your project and to choose an operating framework that allows you to respond to the demands of your goals. The first recommendation is to adopt an agile development methodology.

The distinctive ebb and flow of analytic authoring may mean that there is just no natural end to the project. By being disciplined and systematic with your approach, you can avoid many pitfalls that lead to an under performing project and poorly performing code. Conversely, no amount of innovative, open source software or copious corpus will rescue a project with no structure.

As every data science project is slightly different, there's no right or wrong answers when it comes to overall management...