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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

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

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 big data trends

Big data processing has been an integral part of the IT industry, more so in the past decade. Apache Hadoop and other similar endeavors are focused on building the infrastructure to store and process massive amounts of data. After being around for over 10 years, the Hadoop platform is considered mature and almost synonymous with big data processing. Apache Spark, a general computing engine that works well with is and not limited to the Hadoop ecosystem, was quite successful in the year 2015.

Building data science applications requires knowledge of the big data landscape and what software products are available out of that box. We need to carefully map the right blocks that fit our requirements. There are several options with overlapping functionality, and picking the right tools is easier said than done. The success of the application very much depends on assembling the right mix of technologies and processes. The good news is that there are several open source options...

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