64-bit processors and more than 4 gigs of RAM are relatively new things. Mmap is useless for datasets larger than the disk you can attach to the CPU mmapping it or the size of its virtual address space. In 1991 it would be OK to call a couple billion entries "big data" (although we were yet to invent the term) but not anymore.
Having said that, the heat dissipation of instantiating objects from their on-disk representation is probably a relevant part of the refrigeration costs of any data center. We just have unimaginable computing power available and, sometimes, we do things the easy way, not the most efficient one.
Licensing and hardware will probably be the biggest costs here.
It won't, but 512GB is nowhere near "big data". In fact, most datasets we see should not be called that. My personal rule of thumb is that nothing that can fit in less than a dozen machines can be called "big data". The whole idea begun when we started manipulating datasets that were so huge that moving the data from where it was stored to where it would be processed was no longer practical.
Fortunately we have nice counter-examples with groups such as FoundationDB doing Hadoop scale with strong consistency. Building on the right, proven principles can get one ahead.