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
About the Authors
About the Reviewer
Customer Feedback

About the Reviewer

Sumit Pal is an author who has published SQL on Big Data - Technology, Architecture and Innovations with Apress. Sumit has more than 22 years of experience in the software industry in various roles spanning companies from startups to enterprises.

He is an independent consultant working with big data, data visualization, and data science and a software architect building end-to-end, data-driven analytic systems. 

Sumit has worked for Microsoft (SQL server development team), Oracle (OLAP development team), and Verizon (Big Data analytics team) in a career spanning 22 years. Currently, he works for multiple clients advising them on their data architectures and big data solutions and does hands-on coding with Spark, Scala, Java, and Python. 

Sumit has spoken at big data conferences in Boston, Chicago, Las Vegas, and Vancouver. Sumit is also the author of the book on the same topic published by Apress in October 2016.

He has extensive experience in building scalable systems across the stack from middletier, data tier, to visualization for analytics applications, using BigData and NoSQL DB. Sumit has deep expertise in DataBase Internals, Data Warehouses, Dimensional Modeling, Data Science with Java, Python, and SQL.

Sumit started his career being a part of SQLServer development team at Microsoft in 1996-97 and then as a core server engineer for Oracle Corporation at their OLAP development team in Burlington, MA.

Sumit has also worked at Verizon as an associate director for big data architecture, where he strategized, managed, architected, and developed platforms and solutions for analytics and machine learning applications.

Sumit has also served as a chief architect at ModelN/LeapfrogRX (2006-2013), where he architected the middle tier core analytics platform with open source olap engine (Mondrian) on J2EE and solved some complex dimensional ETL, modeling, and performance optimization problems.

Sumit has done his MS and BS in computer science.

Sumit has hiked to Mt. Everest Base Camp in October 2016.