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)

Summary

Although we were impressed with many of the overall model consistencies, we appreciate that we certainly did not build the most accurate classification system ever. Crowd sourcing this task to millions of users was an ambitious task and by far not the easiest way of getting clearly defined categories. However, this simple proof of concept shows us a few important things:

  1. It technically validates our Spark Streaming architecture.
  2. It validates our assumption of bootstrapping GDELT using an external dataset.
  3. It made us lazy, impatient, and proud.
  4. It learns without any supervision and eventually gets better at every batch.

No data scientist can build a fully functional and highly accurate classification system in just a few weeks, especially not on dynamic data; a proper classifier needs to be evaluated, trained, re-evaluated, tuned, and retrained for at least the first few months, and then re-evaluated every half a year at the very least. Our goal here was to describe the components involved...