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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

Chapter 6. Slick – A Functional Interface for SQL

In Chapter 5, Scala and SQL through JDBC, we investigated how to access SQL databases with JDBC. As interacting with JDBC feels somewhat unnatural, we extended JDBC using custom wrappers. The wrappers were developed to provide a functional interface to hide the imperative nature of JDBC.

With the difficulty of interacting directly with JDBC from Scala and the ubiquity of SQL databases, you would expect there to be existing Scala libraries that wrap JDBC. Slick is such a library.

Slick styles itself as a functional-relational mapping library, a play on the more traditional object-relational mapping name used to denote libraries that build objects from relational databases. It presents a functional interface to SQL databases, allowing the client to interact with them in a manner similar to native Scala collections.

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