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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

Do I need a backend?

In the previous chapter, we learned about the client-server model that underpins how the internet works: when you enter a website URL in your browser, the server serves HTML, CSS, and JavaScript to your browser, which then renders it in the appropriate manner.

What does this all mean for you? Arguably the second question that you should be asking yourself when building a web application is whether you need to do any server-side processing (right after "is this really going to be worth the effort?"). Could you just create an HTML web-page with some JavaScript?

You can get away without a backend if the data needed to build the whole application is small enough: typically a few megabytes. If your application is larger, you will need a backend to transfer just the data the client currently needs. Surprisingly, you can often build visualizations without a backend: while data science is accustomed to dealing with terabytes of data, the goal of the data science process...

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