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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

Towards a web application: HTML templates

In the previous chapter, we briefly saw how to construct HTML templates by interleaving Scala snippets in an HTML file. We saw that templates are compiled to Scala functions, and we learned how to call these functions from the controllers.

In single-page applications, the majority of the logic governing what is actually displayed in the browser resides in the client-side JavaScript, not in the server. The pages served by the server contain the bare-bones HTML framework.

Let's create the HTML layout for our application. We will save this in views/index.scala.html. The template will just contain the layout for the application, but will not contain any information about any user's repositories. To fetch that information, the application will have to query the API developed in the previous chapter. The template does not take any parameters, since all the dynamic HTML generation will happen client-side.

We use the Bootstrap grid layout to control...

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