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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

Functional wrappers for JDBC


We now have a basic overview of the tools afforded by JDBC. All the objects that we have interacted with so far feel somewhat clunky and out of place in Scala. They do not encourage a functional style of programming.

Of course, elegance is not necessarily a goal in itself (or, at least, you will probably struggle to convince your CEO that he should delay the launch of a product because the code lacks elegance). However, it is usually a symptom: either the code is not extensible or too tightly coupled, or it is easy to introduce bugs. The latter is particularly the case for JDBC. Forgot to check wasNull? That will come back to bite you. Forgot to close your connections? You'll get an "out of memory exception" (hopefully not in production).

In the next sections, we will look at patterns that we can use to wrap JDBC types in order to mitigate many of these risks. The patterns that we introduce here are used very commonly in Scala libraries and applications. Thus,...

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