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

Using the Accumulo database


We have seen a method to read our personRdd object from Elasticsearch and this forms a simple and neat solution for our storage requirements. However, when writing commercial applications, we must always be mindful of security and, at the time of writing, Elasticsearch security is still in development; so it would be useful at this stage to introduce a storage mechanism with native security. This is an important consideration we are using GDELT data that is, of course, open source by definition. In a commercial environment, it is very common for datasets to be confidential or commercially sensitive in some way, and clients will often request details of how their data will be secured long before they discuss the data science aspect itself. It is the authors experience that many a commercial opportunity is lost due to the inability of solution providers to demonstrate a robust and secure data architecture.

Accumulo (http://accumulo.apache.org) is a NoSQL database...