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Does someone have an overview with respect to advantages/disadvantages of SparkR vs sparklyr? Google does not yield any satisfactory results and both seem fairly similar. Trying both out, SparkR appears a lot more cumbersome, whereas sparklyr is pretty straight forward (both to install but also to use, especially with the dplyr inputs). Can sparklyr only be used to run dplyr functions in parallel or also "normal" R-Code?

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Sparklyr is an effective tool for interfacing with large datasets in an interactive environment. Simply we can say it is an R interface for apache Spark.  It filters and aggregates Spark datasets and then bring them into R for analysis and visualization.

Through Sparklyr gives you the capability to use Spark as the backend for dplyr, that is a popular data manipulation package.

Sparklyr provides a range of functions that allow you to access the Spark tools for transforming/pre-processing data.


 

SparkR is basically a tool for running R on Spark. In order to use SparkR, we just import it into our environment and run our code. It is similar to the Python API except that it follows R’s syntax instead of Python. For the most part, almost everything available in Python is available in SparkR.

Choosing the winner between these two depends on an individual, i.e. which parameters he/she prefers.

According to my understanding when learning sparkR together with scala-spark api, seem's to be much easier than learning sparklyr which is much more different at least in my perspective.

However sparklyr is more powerful as it supports dplyr, Spark ML and H2O.

The advantages of using sparklyr are:

  • Better data manipulation through compatibility with dpylr

  • Better function naming conventions

  • Better tools for quickly evaluating ML models

  • Easier to run arbitrary code on a Spark DataFrame

If you want to know more about Spark, then do check out this awesome video tutorial:

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