Book Image

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
Book Image

Mastering Spark for Data Science

By: Bifet, Morgan, Amend, Hallett, 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 (15 chapters)

GDELT dimensional modeling


As we have chosen to use GDELT for analysis purposes in this book, we will introduce our first example using this dataset. First, let's select some data.

There are two streams of data available: Global Knowledge Graph (GKG) and Events.

For this chapter, we are going to use GKG data to create a time-series dataset queryable from Spark SQL. This will give us a great starting point to create some simple introductory analytics.

In the next chapters, Chapter 4, Exploratory Data Analysis and Chapter 5, Spark for Geographic Analysis, we'll go into more detail but stay with GKG. Then, in Chapter 7Building Communities, we will explore events by producing our own network graph of persons and using it in some cool analytics.

GDELT model

GDELT has been around for more than 20 years and, during that time, has undergone some significant revisions. For our introductory examples, to keep things simple, let's limit our range of data from 1st April 2013, when GDELT had a major file...