It depends on the requirements of your data model. Anybody with a modicum of competence can scale a point cloud but real-world non-point geometry models is where systems like Oracle have difficulty. Polygons, lines, vectors, etc are a real problem. So-called "spatial hashing" (it had a different name in the 1970s and again in the early 1990s -- the wheel of computer science) has a number of real limitations which is why it was never really used (Oracle has patents on it that have already expired!).

Also, traditional transactional database engines are not designed for the kinds of insert/update rates that are common for many spatial applications. This is a problem for machine-generated data sources generally. It requires an architecture designed specifically for that kind of (ab)use case.

How would you do it then?

I mean, if you wanted to implement a 3d spatial db like that yourself?

This a big topic but there are two main components.

First, you need a storage engine architectures that is designed for very fast appends concurrent with queries. This is trickier than it sounds because you can't use secondary indexes and queries still needs to be efficient. Some recent database engines focused on non-batch "real-time analytics" are designed for this; it is a different internal model than traditional distributed analytic engines. Database engine boffins know ways of achieving this, esoteric but well-understood.

Second, you need a distributed interval index i.e. a distributed data structure that can act as an efficient index for 3-dimensional cube types. Scalable distributed interval indexing requires that data models be embedded in a higher dimensionality space, so at least 4-dimensions. The well-known example from literature is multi-level grids but those have many limitations. The state-of-the-art structures are adaptive spatial sieves; advanced versions are computationally efficient even for very high dimensionality cubes. However, these algorithms are encumbered and little has been published on them. (Disclosure: I am the inventor of the first useful spatial sieve algorithms. The idea dates back to at least 1990 but had unsolved theoretical issues until 2007.)

I am building a real-time analytical database similar to this right now, and petabyte-scale 3-dimensional spatial data models are a core part of its functionality. Building fast, distributed 3d spatial databases are achievable, it just requires a different data structures and algorithms skill set than you would use for more traditional database designs.