How does R handle large-scale distributed joins in heterogeneous data systems?

R performs distributed joins through Spark, databases, or data.table optimizations depending on system scale.

At enterprise scale, joins are pushed down to distributed engines like Spark SQL or database systems rather than executed in R memory. Spark performs partition-aware joins, minimizing data shuffling. In local systems, data.table uses indexed joins for in-memory performance. The architecture decision is always to push computation down to the storage layer.