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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 5. Scala and SQL through JDBC

One of data science's raison d'être is the difficulty of manipulating large datasets. Much of the data of interest to a company or research group cannot fit conveniently in a single computer's RAM. Storing the data in a way that is easy to query is therefore a complex problem.

Relational databases have been successful at solving the data storage problem. Originally proposed in 1970 (http://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf), the overwhelming majority of databases in active use today are still relational. In that time, the price of RAM per megabyte has decreased by a factor of a hundred million. Similarly, hard drive capacity has increased from tens or hundreds of megabytes to terabytes. It is remarkable that, despite this exponential growth in data storage capacity, the relational model has remained dominant.

Virtually all relational databases are described and queried with variants of SQL (Structured Query Language...

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