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

Content registry


We have seen in this chapter that data ingestion is an area that is often overlooked, and that its importance cannot be underestimated. At this point, we have a pipeline that enables us to ingest data from a source, schedule that ingest, and direct the data to our repository of choice. But the story does not end there. Now we have the data, we need to fulfil our data management responsibilities. Enter the content registry.

We're going to build an index of metadata related to that data we have ingested. The data itself will still be directed to storage (HDFS, in our example) but, in addition, we will store metadata about the data, so that we can track what we've received and understand basic information about it, such as, when we received it, where it came from, how big it is, what type it is, and so on.

Choices and more choices

The choice of which technology we use to store this metadata is, as we have seen, one based upon knowledge and experience. For metadata indexing, we...