Because when you have a DAG of 100s of CREATEs that need to be run in dependency, many of which are table materializations and not views, you will end up creating a poorly specified, half baked version of DBT.

Here's why I like it:

- Dependency tracking

- Not just views, but tables, and incremental tables

- Testing framework

I've worked on a few data teams that have all been "copy/paste SQL from GitHub into the Query Editor" and it's (obviously) pretty bad. DBT is super low-lift and professionalizes your data pipeline basically instantly.

My top three:

- Dev/stag/prod env check numbers before pushing to production.

- Unions between two sources that are not the same shape can be done without the headache. https://github.com/dbt-labs/dbt-utils#union_relations-source

- Macros for common case when statements.

Create view is a great V0.5 for a data warehouse and what I recommend people do if possible so they can concentrate on building the right schema, naming standards, etc.

dbt is the V1. You get a lot of tooling, including a proper dag, logging, parametrization. You also get the ability to easily materialize your tables in a convenient format, which is important if (probably when) you figure out consistency is important. Views can take you far, but most orgs will eventually need more, and dbt is designed to be exactly that.

As a side note, moving from views to dbt is actually quite easy. I've done it several times and it's usually taken a couple of developer days to get started and maybe a couple weeks to fully transition.