The problem with modeling data isn't the lack of tools or the need to approach this problem differently. Plenty of solutions exist. People just don't care, an

The problem with modeling data isn't the lack of tools or the need to approach this problem differently. Plenty of solutions exist. People just don't care, and this is ok. It used to be that you needed tools like Informatica and Kimball inspired datamarts, but databases are now bigger and faster. Whatever data modeling problems you may have, you can easily clean up in an ETL or a BI layer, with relatively little effort. This makes tools like Looker, dbt, and Holistics a luxury and not something you need to have. I wish the industry would put more effort into defining clean data models, but I think that ship has sailed. The prevailing trend seems to be to create Data Lakes, add a BI layer, then call it a day. [edit] Also...some database points. The industry never shifted to using NoSQL to replace RDBMS systems. But event processing matured, NoSQL db's are ideal for storing unstructured data, so you see them in data engineering stacks. Greenplum is a free MPP database that has been around for nearly a decade. The point about Spanner SQL is interesting for the fact that Spanner evolved from NoSQL like methods to a SQL like dialect, but Spanner is a unique flower in the industry, due to being an HTAP db.