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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
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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

Standalone programs

So far, we have been using Spark SQL and DataFrames through the Spark shell. To use it in standalone programs, you will need to create it explicitly, from a Spark context:

val conf = new SparkConf().setAppName("applicationName")
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

Additionally, importing the implicits object nested in sqlContext allows the conversions of RDDs to DataFrames:

import sqlContext.implicits._

We will use DataFrames extensively in the next chapter to manipulate data to get it ready for use with MLlib.

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