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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

Slick versus JDBC


This chapter and the previous one introduced two different ways of interacting with SQL. In the previous chapter, we described how to use JDBC and build extensions on top of JDBC to make it more usable. In this chapter, we introduced Slick, a library that provides a functional interface on top of JDBC.

Which method should you choose? If you are starting a new project, you should consider using Slick. Even if you spend a considerable amount of time writing wrappers that sit on top of JDBC, it is unlikely that you will achieve the fluidity that Slick offers.

If you are working on an existing project that makes extensive use of JDBC, I hope that the previous chapter demonstrates that, with a little time and effort, you can write JDBC wrappers that reduce the impedance between the imperative style of JDBC and Scala's functional approach.

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