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Spark for Data Science

You're reading from Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

Product type Paperback

Published in Sep 2016

Publisher Packt

ISBN-13 9781785885655

Length 344 pages

Edition 1st Edition

Languages

Scala

Tools

IPython

Concepts

Data Science

Authors (2):

Bikramaditya Singhal

Srinivas Duvvuri

View More author details

Table of Contents (12) Chapters

Preface

1. Big Data and Data Science – An Introduction FREE CHAPTER

2. The Spark Programming Model

3. Introduction to DataFrames

4. Unified Data Access

5. Data Analysis on Spark

6. Machine Learning

7. Extending Spark with SparkR

8. Analyzing Unstructured Data

9. Visualizing Big Data

10. Putting It All Together

11. Building Data Science Applications

The programming paradigm

For Spark to address the big data challenges and serve as a platform for data science and other scalable applications, it was built with well-thought-out design considerations and language support.

There are Spark APIs designed for varieties of application developers to create Spark-based applications using standard API interfaces. Spark provides APIs for Scala, Java, R and Python programming languages, as explained in the following sections.

Supported programming languages

With built-in support for so many languages, Spark can be used interactively through a shell, which is otherwise known as Read-Evaluate-Print-Loop (REPL), in a way that will feel familiar to developers of any language. The developers can use the language of their choice, leverage existing libraries, and seamlessly interact with Spark and its ecosystem. Let us see the ones supported on Spark and how they fit into the Spark ecosystem.

Scala

Spark itself is written in Scala, a Java Virtual Machine ...

The rest of the chapter is locked

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Authors (2)

Duvvuri

Duvvuri

Srinivas Duvvuri is currently Senior Vice President Development, heading the development teams for Fixed Income Suite of products at Broadridge Financial Solutions (India) Pvt Ltd. In addition, he also leads the Big Data and Data Science COE and is the principal member of the Broadridge India Technology Council. He is self learnt Data Scientist. The Big Data /Data Science COE in the past 3 years, has successfully completed multiple POCs and some of the use cases are moving towards production deployment. He has over 25+ years of experience in software product development. His experience spans predominantly in product development in, multiple domains Financial Services, Infrastructure Management, OLAP, Telecom Billing and Customer Care, CAD/CAM. Prior to Broadridge, hes held leadership positions at a Startup and leading IT majors such as CA, Hyperion (Oracle), Globalstar. He has a patent in Relational OLAP. Srinivas loves to teach and mentor budding Engineers. He has established strong Academic connect and interacts with a host of educational institutions, He is an active speaker in various conferences, summits and meetups on topics such as Big data, Data Science Srinivas is a B.Tech in Aeronautical Engineering and M.Tech in Computer Science, from IIT, Madras.

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Singhal

Singhal

Manu Singhal has been a programmer for 8 years and loves to code on Ruby and React. These days, he is busy cofounding a startup in e-commerce. In earlier roles, he has developed many enterprise and consumer based web/mobile applications and has also been a speaker at Ruby Conferences in India and the USA. He never misses a chance to play tennis and go hiking. He has worked with Tata Consultancy Services and McKinsey & Company as a software developer and an architect. He has contributed in books on Rhomobile and RubyMotion by Packt earlier.

See other products by Singhal

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