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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

Chapter 7. Web APIs

Data scientists and data engineers get data from a variety of different sources. Often, data might come as CSV files or database dumps. Sometimes, we have to obtain the data through a web API.

An individual or organization sets up a web API to distribute data to programs over the Internet (or an internal network). Unlike websites, where the data is intended to be consumed by a web browser and shown to the user, the data provided by a web API is agnostic to the type of program querying it. Web servers serving HTML and web servers backing an API are queried in essentially the same way: through HTTP requests.

We have already seen an example of a web API in Chapter 4, Parallel Collections and Futures, where we queried the "Markit on demand" API for current stock prices. In this chapter, we will explore how to interact with web APIs in more detail; specifically, how to convert the data returned by the API to Scala objects and how to add additional information...

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