Using Apache SparkR to Power Shiny Applications: Part I



The objective of this blog post is demonstrate how to use Apache SparkR to power Shiny applications. I have been curious about what the use cases for a “Shiny-SparkR” application would be and how to develop and deploy such an app.

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets. SparkR also supports distributed machine learning using MLlib.

Shiny is an open source R package that provides an elegant and powerful web framework for building web applications using R. Shiny helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Launch Apache Spark on AWS EC2 and Initialize SparkR Using RStudio



In this blog post, we shall learn how to launch a Spark stand alone cluster on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) for analysis of Big Data. This is a continuation from our previous blog, which showed us how to download Apache Spark and start SparkR locally on windows OS and RStudio.

We shall use Spark 1.5.1 (released on October 02, 2015) which has a spark-ec2 script that is used to install stand alone Spark on AWS EC2.  A nice feature about this spark-ec2 script is that it installs RStudio server as well. This means that you don’t need to install RStudio server separately. Thus you can start working with your data immediately after Spark is installed.