So our company adopted databricks earlier 2021, through niavity databricks was going to be the "source of truth" for all our data and application. We could build apps, report and do analysis all in one place, it seemed like the holy grail of consolidate everything into one place.

Our organization has since moved away from this model and now all applications are linked to databricks but hold their own datastroage. In hindsight it was not a smart idea to make databricks the one database to rule them all.

The more we pinned applications to the databricks data model the more fragile things became, application coulpling slowed down teams, there was a dilution of what data mattered in our datalake/warehouse.

Handilng sesion/ application specific state was increasingly painful, as well as scaling the apps.

We were always compromising the application to make things work the pyspark/databricks way. Even managing application data inside of databricks just added extra complextity and clunkiness.

The schemas, ETL and processing became intertwined to the extent the teams were feeling like they were fighting a loosing battle daily where one change would break other things.

Yes, the accessiblity of data was "easy" but at a cost of most other things.

IMHO (and as written in the databricks docs) Databricks is a lakehouse/ warehouse, and as such it should be kept that way. You can add applications to databricks and it will seem fast and easy until you are comprosing and fighting fires daily.