With reference to the database, as long as I use spec to strictly define (standardization) the core data model, I can ensure the correctness of the system.

I've turned the traditional work of ensuring code correctness from in-code type system validation to data self-validation. Turn the work into verifying the core data model instead of validating the input and output data types of the function.

A system requires only a few core data models, and development is built around the core data model.

Persistent data structures ensure that the modification of the immutable big data model are high performance and memory efficient.

In addition, using my pure function pipeline data stream (https://github.com/linpengcheng/PurefunctionPipelineDataflow) makes debugging, parallelism and extension very simple.

Similar to industry, verify that all finished products meet the standards before entering the warehouse.

Also similar to databases, verify their compliance before data enters the database.

That is "Data as a service, Warehouse as the core, operates around it".

I think their point is that it's the ability to express relational algebra in a natural way that's important, and the static/dynamic types thing is orthogonal to that.