Earlier quoted context omitted.
Sometimes yes, I've seen it. It even tends to happen on NoSQL databases as well. Three times I've seen apps start on top of Dynamo DB, and then end up re-implementing relational databases at the application level anyway. Starting with postgres would have been the right answer for all three of those. Initial dev went faster, but tech debt and complexity quickly started soaking up all those gains and left a hard-to-mai…
This always confuses me because we have decades of SQL and all its issues as well. Hundreds of experienced devs talking about all the issues in SQL and the quirks of queries when your data is not trivial. One would think that for a startup of sorts, where things changes fast and are unpredictable, NoSQL is the correct answer. And when things are stable and the shape of entities are known, going for SQL becomes a natu…
A lot of the bespoke no-sql data stores really started to come to the forefront around 2010 or so. At that time, having 8 cpu cores and 10k rpm SAS spinning drives was a high end server. Today, we have well over 100 cores, with TBs of RAM and PCIe Gen 4/5 NVME storage (u.x) that is thousands of times faster and has a total cost lower than the servers from 2010 or so that your average laptop can outclass today.
You can vertically scale a traditional RDBMS like PostgreSQL to an extreme degree... Not to mention utilizing features like JSONB where you can have denormalized tables within a structured world. This makes it even harder to really justify using NoSQL/NewSQL databases. The main bottlenecks are easier to overcome if you relax normalization where necessary.
There's also the consideration of specialized databases or alternative databases where data is echo'd to for the purposes of logging, metrics or reporting. Not to mention, certain layers of appropriate caching, which can still be less complex than some multi-database approaches.