Overview

• Discuss the need for distributed computing
• Illustrate the split-apply-combine analytical pattern
• Define parallel processing
• Define SQL
• Demonstrate how to access local and remote SQL databases
• Introduce Hadoop and Spark as distributed computing platforms
• Introduce the sparklyr package
• Demonstrate how to use sparklyr for machine learning using the Titanic data set

Before class

• Install sparklyr and H2O on your local computer. Run the code below to install all necessary packages and set the correct options.

  install.packages(c("sparklyr", "rsparkling"))
  options(rsparkling.sparklingwater.version = "2.1.0")
  
  library(sparklyr)
  spark_install(version = "2.1.0")
  

  Last year, 70% of students were able to successfully install these packages without problems. The others ran into problems. Make sure to attempt installing these packages before class so if you have errors we can debug them before you need to use the packages.

Class materials

• Accessing databases using dbplyr

• Split-apply-combine and parallel computing

• Spark and sparklyr

• The split-apply-combine strategy for data analysis - paper by Hadley Wickham establishing a general overview of split-apply-combine problems. Note that the plyr package is now deprecated in favor of dplyr and the other tidyverse packages

• Accessing databases using dbplyr

• Taxi dataset

• bigrquery - instructions for setting up an account to access Google Bigquery databases

• sparklyr - introduction to the sparklyr interface for Spark

What you need to do after class