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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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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

Introduction to web frameworks

This section is a brief introduction to how modern web applications are designed. Go ahead and skip it if you already feel comfortable writing backend code.

Loosely, a web framework is a set of tools and code libraries for building web applications. To understand what a web framework provides, let's take a step back and think about what you would need to do if you did not have one.

You want to write a program that listens on port 80 and sends HTML (or JSON or XML) back to clients that request it. This is simple if you are serving the same file back to every client: just load the HTML from file when you start the server, and send it to clients who request it.

So far, so good. But what if you now want to customize the HTML based on the client request? You might choose to respond differently based on part of the URL that the client put in his browser, or based on specific elements in the HTTP request. For instance, the product page on amazon.com is different...

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