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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

Authentication – adding HTTP headers


So far, we have been using the GitHub API without authentication. This limits us to sixty requests per hour. Now that we can query the API in parallel, we could exceed this limit in seconds.

Fortunately, GitHub is much more generous if you authenticate when you query the API. The limit increases to 5,000 requests per hour. You must have a GitHub user account to authenticate, so go ahead and create one now if you need to. After creating an account, navigate to https://github.com/settings/tokens and click on the Generate new token button. Accept the default settings and enter a token description and a long hexadecimal number should appear on the screen. Copy the token for now.

HTTP – a whirlwind overview

Before using our newly generated token, let's take a few minutes to review how HTTP works.

HTTP is a protocol for transferring information between different computers. It is the protocol that we have been using throughout the chapter, though Scala hid the details...

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