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 Timely as a time series database


Now that we are able to transform raw information into a clean series of Twitter sentiment with parameters such as hashtags, emojis, or US states, such a time series should be stored reliably and made available for fast query lookups.

In the Hadoop ecosystem, OpenTSDB (http://opentsdb.net/) is the default database for storing millions of chronological data points. However, instead of using the obvious candidate, we will introduce one you may not have come across before, called Timely (https://nationalsecurityagency.github.io/timely/). Timely is a recently open sourced project started by the National Security Agency (NSA), as a clone of OpenTSDB, which uses Accumulo instead of HBase for its underlying storage. As you may recall, Accumulo supports cell-level security, and we will see this later on.

Storing data

Each record is composed of a metric name (for example, hashtag), timestamp, metric value (for example, sentiment), an associated set of tags (for...