You can plug any compute kernel you want into spark, that's not a pro or con of R.
Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'?
R does not have have meaningful out of core compute offerings that compare with something like Dask.
R does not at all have cluster compute offerings that compare to Dask Distributed.
If you want to know what real performance looks like, check out Python's cudf which will shortly fully match the Pandas api. That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here.
Whatever advantages R has, perf or scalability are definitely not amongst them.
> That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here.
I don't see how the "GPU DataFrames" provided in cuDF would enhance a raytracer in any way.
No, I'm not. I'm well aware of CUDA being used to accelerate raytracing. That cannot be accomplished by simply providing a GPU-accelerated data frame structure, as cuDF provides.
Column stores are standard in any analytics pipeline today. They make up Python's Pandas, R's dplyr, and Java's DataFrame. How or why does R stand out for 'massive amounts of data'?
R does not have have meaningful out of core compute offerings that compare with something like Dask.
R does not at all have cluster compute offerings that compare to Dask Distributed.
If you want to know what real performance looks like, check out Python's cudf which will shortly fully match the Pandas api. That raytracing example you linked would run at interactive rates with cudf, I really don't see any basis for perf arguments in R's favour, and 'massive data' arguments are laughable here.
Whatever advantages R has, perf or scalability are definitely not amongst them.