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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Client-server applications

A website works through the interaction between two computers: the client and the server. If you enter the URL www.github.com/pbugnion/s4ds/graphs in a web browser, your browser queries one of the GitHub servers. The server will look though its database for information concerning the repository that you are interested in. It will serve this information as HTML, CSS, and JavaScript to your computer. Your browser is then responsible for interpreting this response in the correct way.

If you look at the URL in question, you will notice that there are several graphs on that page. Unplug your internet connection and you can still interact with the graphs. All the information necessary for interacting with the graphs was transferred, as JavaScript, when you loaded that webpage. When you play with the graphs, the CPU cycles necessary to make those changes happen are spent on your computer, not a GitHub server. The code is executed client-side. Conversely, when you request...

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