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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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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

Rest APIs: best practice

As the Internet matures, REST (representational state transfer) APIs are emerging as the most reliable design pattern for web APIs. An API is described as RESTful if it follows these guiding principles:

  • The API is designed as a set of resources. For instance, the GitHub API provides information about users, repositories, followers, etc. Each user, or repository, is a specific resource. Each resource can be addressed through a different HTTP end-point.
  • The URLs should be simple and should identify the resource clearly. For instance, api.github.com/users/odersky is simple and tells us clearly that we should expect information about the user Martin Odersky.
  • There is no world resource that contains all the information about the system. Instead, top-level resources contain links to more specialized resources. For instance, the user resource in the GitHub API contains links to that user's repositories and that user's followers, rather than having all that information...
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