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  • Book Overview & Buying Mastering Spark for Data Science
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Mastering Spark for Data Science

Mastering Spark for Data Science

By : Bifet, Morgan, Amend, Hallett, George
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Mastering Spark for Data Science

Mastering Spark for Data Science

4 (2)
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)
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Names de-duplication


As we were pulling entities from an NLP extraction process without any validation, the name we were able to retrieve may be written in many different ways. They can be written in different order, might contain middle names or initials, a salutation or a nobility title, nicknames, or even some typos and spelling mistakes. Although we do not aim to fully de-duplicate the content (such as learning that both Ziggy Stardust and David Bowie stand for the same person), we will be introducing two simple techniques used to de-duplicate a large amount of data at a minimal cost by combining the concept MapReduce paradigm and functional programming.

Functional programming with Scalaz

This section is all about enriching data as part of an ingestion pipeline. We are therefore less interested in building the most accurate system using advanced machine learning techniques, but rather the most scalable and efficient one. We want to keep a dictionary of alternative names for each record...

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Mastering Spark for Data Science
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