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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
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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

Model-View-Controller architecture

Many web frameworks impose program architectures: it is difficult to provide wires to bind disparate components together without making some assumptions about what those components are. The Model-View-Controller (MVC) architecture is particularly popular on the Web, and it is the architecture the Play framework assumes. Let's look at each component in turn:

  • The model is the data underlying the application. For example, I expect the application underlying GitHub has models for users, repositories, organizations, pull requests and so on. In the Play framework, a model is often an instance of a case class. The core responsibility of the model is to remember the current state of the application.
  • Views are representations of a model or a set of models on the screen.
  • The controller handles client interactions, possibly changing the model. For instance, if you star a project on GitHub, the controller will update the relevant models. Controllers normally carry...
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