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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 11. Spark SQL and DataFrames

In the previous chapter, we learned how to build a simple distributed application using Spark. The data that we used took the form of a set of e-mails stored as text files.

We learned that Spark was built around the concept of resilient distributed datasets (RDDs). We explored several types of RDDs: simple RDDs of strings, key-value RDDs, and RDDs of doubles. In the case of key-value RDDs and RDDs of doubles, Spark added functionality beyond that of the simple RDDs through implicit conversions. There is one important type of RDD that we have not explored yet: DataFrames (previously called SchemaRDD). DataFrames allow the manipulation of objects significantly more complex than those we have explored to date.

A DataFrame is a distributed tabular data structure, and is therefore very useful for representing and manipulating structured data. In this chapter, we will first investigate DataFrames through the Spark shell, and then use the Ling-spam...

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