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Mastering Spark for Data Science

You're reading from Mastering Spark for Data Science

Product type Book

Published in Mar 2017

Publisher Packt

ISBN-13 9781785882142

Pages 560 pages

Edition 1st Edition

Languages

Concepts

Authors (4):

Andrew Morgan

Antoine Amend

Matthew Hallett

David George

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Table of Contents (22) Chapters

Mastering Spark for Data Science

Credits

Foreword

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

The Big Data Science Ecosystem

Data Acquisition

Input Formats and Schema

Exploratory Data Analysis

Spark for Geographic Analysis

Scraping Link-Based External Data

Building Communities

Building a Recommendation System

News Dictionary and Real-Time Tagging System

Story De-duplication and Mutation

Anomaly Detection on Sentiment Analysis

TrendCalculus

Secure Data

Scalable Algorithms

Parquet

Apache Parquet is a columnar storage format specifically designed for the Hadoop ecosystem. Traditional row-based storage formats are optimized to work with one record at a time, meaning they can be slow for certain types of workload. Instead, Parquet serializes and stores data by column, thus allowing for optimization of storage, compression, predicate processing, and bulk sequential access across large datasets - exactly the type of workload suited to Spark!

As Parquet implements per column data compaction, it's particularly suited to CSV data, especially with fields of low cardinality, and file sizes can see huge reductions when compared to Avro.

+--------------------------+--------------+ 
|                 File Type|          Size| 
+--------------------------+--------------+ 
|20160101020000.gkg.csv    |      20326266| 
|20160101020000.gkg.avro   |      13557119| 
|20160101020000.gkg.parquet|       6567110| 
|20160101020000.gkg.csv.bz2|       4028862...

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Authors (4)

Andrew 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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Antoine 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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Matthew 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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David 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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