![]() ![]() It’s not that widespread and very verbose. There’s a Java API as well, not much to say over here. If you are using PySpark right now, there aren’t that many reasons to make you switch to either R or Scala. Python can also be used to work with Spark and is a good mix between the advantages of R and Scala. Also, SparkR is usually the last API to receive updates. Some of the niceties of Spark are not readily available in R. The R binding for Spark is a more recent addition. However, I have discovered that when it comes to doing data science specific operations, Scala is not always the easiest language to work with. Moreover, the latest features are first available in the Scala API. Using Scala with Spark is consistent, reliable and easy. They do not feel like applying ML algorithms from scratch and would more likely be interested in using libraries that someone else battle tested first. ![]() COULD NOT FIND FUNCTION SPARKR.SESSION IN SPARKR SOFTWAREAnother audience of this article are software engineers used to working with Scala and Spark who need to implement custom machine learning projects at scale.Many are now facing the challenges of Big Data. Data scientists and statisticians who prefer using R or do not have Python experience.When it comes to doing Machine Learning with Spark, this article targets two types of people: In this article I plan on touching a few key points about using Spark with R, focusing on the Machine Learning part of it. It is mostly used with Scala and Python, but the R based API is also gaining a lot of popularity. Spark is the ubiquitous Big Data framework that makes it easy to process data at scale. ![]()
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