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

Building stories

Simhash should be used to detect near-duplicate articles only. Extending our search to a 3-bit or 4-bit difference becomes terribly inefficient (3-bit difference requires 5,488 distinct queries to Cassandra while 41,448 queries will be needed to detect up to 4-bit differences) and seems to bring much more noise than related articles. Should the user want to build larger stories, a typical clustering technique must be applied then.

Building term frequency vectors

We will start grouping events into stories using a KMeans algorithm, taking the articles' word frequencies as input vectors. TF-IDF is simple, efficient, and a proven technique to build vectors out of text content. The basic idea is to compute a word frequency that we normalize using the inverse document frequency across the dataset, hence decreasing the weight on common words (such as stop words) while increasing the weight of words specific to the definition of a document. Its implementation is part of the basics...