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

Spark architecture

Apache Spark is designed to simplify the laborious, and sometimes error prone task of highly-parallelized, distributed computing. To understand how it does this, let's explore its history and identify what Spark brings to the table.

History of Spark

Apache Spark implements a type of data parallelism that seeks to improve upon the MapReduce paradigm popularized by Apache Hadoop. It extended MapReduce in four key areas:

  • Improved programming model: Spark provides a higher level of abstraction through its APIs than Hadoop; creating a programming model that significantly reduces the amount of code that must be written. By introducing a fluent, side-effect-free, function-oriented API, Spark makes it possible to reason about an analytic in terms of its transformations and actions, rather than just sequences of mappers and reducers. This makes it easier to understand and debug.

  • Introduces workflow: Rather than chaining jobs together (by persisting results to disk and using a third...