[iamlucaswolf]

I think this question mixes up two orthogonal dimensions: transactionality (i.e., OLTP vs. OLAP) and data model (relational, document-based, graph, vector, ...).

Regarding transactionality: There is an entire area of research on "hybrid transactional and analytical processing" (HTAP) systems that unifies OLAP and OLTP systems. Hyper [1] pioneered this path at TU Munich, it's successor Umbra [2] recently incorporated as CedarDB [3]. There are lots of others. Most of these systems, AFAIK, are relational.

Regarding data model: What we've seen in the past few decades is that non-relational DBMS (excluding key-value stores) only make sense in rare edge cases that require huge scale. There has, e.g. been research [4] that shows that graph databases are still, well, lacking, compared to relational systems. The common pattern seems to be: unless you need to service very specific workloads at huge scales, SQL is probably enough [5]. Then again, it really comes down to intrinsics. If you were to, for example, implement distributed locking using Postgres, you would likely run into problems with MVCC and Xids very quickly.

So, as you already mentioned, there is no silver bullet. But even today, unless you are Meta or Google, SQL is probably enough for a long time and lots of use cases.

(Full disclosure: I'm working on Hyper full-time).

[1]: https://hyper-db.de/ [2]: https://umbra-db.com/ [3]: https://cedardb.com/ [4]: https://homepages.cwi.nl/~boncz/edbt2022.pdf [5]: https://www.youtube.com/watch?v=VxKt245X_ws