That's like saying "how to get to the moon is obvious: traveling"
It's not exactly rocket science.
I still feel like you're underselling the article however.
Is obviously ultimately parallelism, but parallelism is hard at scale - because things often don't scale - and incorrect parallelism can even make things slower. And it's not always obvious why something gets slower by parallelism.
As a dumb example, if you have a fictional HDD with one disk and one head, you have two straightforward options to optimize performance:
Make sure only one file is read at the same time (otherwise the disk will keep seeking back and forth)
Make sure the file is persisted in a way that you're only accessing one sector, never entering the situation in which it would seek back and forth.
Ofc, that can be dumped down to "parallelism", because this is inherently a question about how to parallelize... But it's also ignoring that that's what is being elaborated on: ways s3 used to enable parallelism
Data gets split into redundant copies, and is rebalanced in response to hot spots.
Everything in this article is the obvious answer you'd expect.
It just saves AWS money is all, by achieving greater redundancy with less disk usage.
They shard the data as you might expect of a RAID, 5, 6, etc array and the distributed parity solves the problem of failure tolerance as you would expect and also improves bandwidth via parallelism as you describe.
The interesting part is their best strategy for sharding the data: plain-old-simple random. The decision of which disks and at which sectors to shard the data is done at random, and this creates the best change that at least one of the two copies of data can be accessed with much lower latency (~1ms instead of ~8ms).
The most crude, simple approach turns out to give them the best mileage. There's something vaguely poetic about it, an aesthetic beauty reminiscent of Euler's Identity or the solution to the Basel Problem; a very simple statement with powerful implications.