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Mastering Spark for Data Science

You're reading from Mastering Spark for Data Science Lightning fast and scalable data science solutions

Product type Paperback

Published in Mar 2017

Publisher Packt

ISBN-13 9781785882142

Length 560 pages

Edition 1st Edition

Tools

Apache Spark

Concepts

Data Science

Authors (5):

Bifet

Morgan

Amend

Hallett

George

+1 more

View More author details

Table of Contents (22) Chapters

Mastering Spark for Data Science

Credits

Foreword

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. The Big Data Science Ecosystem FREE CHAPTER

2. Data Acquisition

3. Input Formats and Schema

4. Exploratory Data Analysis

5. Spark for Geographic Analysis

6. Scraping Link-Based External Data

7. Building Communities

8. Building a Recommendation System

9. News Dictionary and Real-Time Tagging System

10. Story De-duplication and Mutation

11. Anomaly Detection on Sentiment Analysis

12. TrendCalculus

13. Secure Data

14. Scalable Algorithms

Spark architecture

Apache Spark is designed to simplify the laborious, and sometimes error prone task of highly-parallelized, distributed computing. To understand how it does this, let's explore its history and identify what Spark brings to the table.

History of Spark

Apache Spark implements a type of data parallelism that seeks to improve upon the MapReduce paradigm popularized by Apache Hadoop. It extended MapReduce in four key areas:

Improved programming model: Spark provides a higher level of abstraction through its APIs than Hadoop; creating a programming model that significantly reduces the amount of code that must be written. By introducing a fluent, side-effect-free, function-oriented API, Spark makes it possible to reason about an analytic in terms of its transformations and actions, rather than just sequences of mappers and reducers. This makes it easier to understand and debug.
Introduces workflow: Rather than chaining jobs together (by persisting results to disk and using a third...

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Authors (5)

Bifet

Bifet

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Morgan

Morgan

Andrew Morgan is a specialist in data strategy and its execution, and has deep experience in the supporting technologies, system architecture, and data science that bring it to life. With over 20 years of experience in the data industry, he has worked designing systems for some of its most prestigious players and their global clients often on large, complex and international projects. In 2013, he founded ByteSumo Ltd, a data science and big data engineering consultancy, and he now works with clients in Europe and the USA. Andrew is an active data scientist, and the inventor of the TrendCalculus algorithm. It was developed as part of his ongoing research project investigating long-range predictions based on machine learning the patterns found in drifting cultural, geopolitical and economic trends. He also sits on the Hadoop Summit EU data science selection committee, and has spoken at many conferences on a variety of data topics. He also enjoys participating in the Data Science and Big Data communities where he lives in London.

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Amend

Amend

Antoine Amend is a data scientist passionate about big data engineering and scalable computing. The books theme of torturing astronomical amounts of unstructured data to gain new insights mainly comes from his background in theoretical physics. Graduating in 2008 with a Msc. in Astrophysics, he worked for a large consultancy business in Switzerland before discovering the concept of big data at the early stages of Hadoop. He has embraced big data technologies ever since, and is now working as the Head of Data Science for cyber security at Barclays Bank. By combining a scientific approach with core IT skills, Antoine qualified two years running for the Big Data World Championships finals held in Austin TX. He Placed in the top 12 in both 2014 and 2015 edition (over 2000+ competitors) where he additionally won the Innovation Award using the methodologies and technologies explained in this book.

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Hallett

Hallett

Matthew Hallett is a Software Engineer and Computer Scientist with over 15 years of industry experience. He is an expert Object Oriented programmer and systems engineer with extensive knowledge of low level programming paradigms and, for the last 8 years, has developed an expertise in Hadoop and distributed programming within mission critical environments, comprising multithousandnode data centres. With consultancy experience in distributed algorithms and the implementation of distributed computing architectures, in a variety of languages, Matthew is currently a Consultant Data Engineer in the Data Science & Engineering team at a top four audit firm.

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George

George

David George is a distinguished distributed computing expert with 15+ years of data systems experience, mainly with globally recognized IT consultancies and brands. Working with core Hadoop technologies since the early days, he has delivered implementations at the largest scale. David always takes a pragmatic approach to software design and values elegance in simplicity. Today he continues to work as a lead engineer, designing scalable applications for financial sector customers with some of the toughest requirements. His latest projects focus on the adoption of advanced AI techniques for increasing levels of automation across knowledge-based industries.

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