You can see that with so called "Big Data" tools. Those which originated as databases (Mongo), ended up adding "Map-reduce" feature. Those which started as map-reduce tools evolved to support SQL (Hadoop->Spark). Those which started as SQL engines (Spark) added support for streaming, while those who started as steaming platforms (Kafka) added SQL support (KSQLDB). Traditional DB engines evolve to allow document data (Postgres with JSON column type). One more decade until one-tool-to-rule-them-all emerges :)
It's the Cassandra curse of computer scientists. Business people think they don't have time or resources for correctness, and end up forcing incorrect systems to be held by hand. Which, despite the high cost of this labor, is a decent trade-off because thanks to the internet business models have very low marginal cost per costumer. I think that the fact that Netflix has a very high marginal cost per costumer and actually hires very highly qualified programmers and pioneers systems designs illustrates my point.
