I would say that most resources would be a mixed bag in most situations but much of the advice skips over something very basic that experienced people tend to assume you know: you are always going to be limited by the number of times you can actually sync a write to a disk and/or how fast you can read back from the disk and these are the main upper bounds. One mistake I see inexperienced engineers make in modern environments is not realizing that in their particular cloud instance they are explicitly or implicitly using the cloud vendor's network storage and they are entirely hamstrung on that. The other is not configuring the database to use all the RAM- but those are usually covered by the existing tuning guides.

The next from a design perspective is to leave natural places to shard/split the data up so you could represent the data consistently on multiple systems. Sometimes it's pretty bonehead things like your database tables related to login are affecting your critical path transactions and/or also tied to the referential integrity of your application data in ways that are hard to split apart. It goes back to the IO thing where if you are reading and writing login or UI traffic you sure as heck are not writing some other critical time sensitive transaction especially if it's to the same disk. The other obvious thing is to be trying to run analytic queries (aka look at many records) on a database tuned for mass INSERT/write traffic and having no plan to do that with a different resource.

If you are still worried about it I would suggest just prototyping it using a currently modern API/RPC server in front of whatever datastore you come up with. As systems have gotten more distributed the straight line speed of connecting to the database directly makes less and less sense in my opinion and having an API server you control allows you to be clever later in ways that might not be obvious today (especially keeping application-aware counters so that you can see what your application is actually doing as query-style logs for databases are extremely expensive in performance). Either way if you sit down and consider the performance characteristics of the underlying system, think about each class of transactions are you considering from your application in a write/read context (hopefully from measured data), and using tools like EXPLAIN you'll quickly start finding what pieces of the puzzle might need to move to start scaling.

The answer also might just be some parts of your application would be better served something other than a database like a message queue, a more straightforward ISAM or key-value store, or depending on your situation just flat files written onto the fastest local storage you can get to then be later transformed and loaded into a database sometime later.