Do you agree or disagree from this feedback?

https://www.youtube.com/watch?v=f180l9_edsA

The primary take-away for me was the idea of logical data warehouses. These are data warehouses that combine database types. For example, they combine relational and Hadoop systems into a single warehouse. The benefit of such a structure was shown through the lending example, where a lending company used both metrics stored in a relational database and information stored in Hadoop (social media posts and tweets from customers) to analyze whether a company would pay back a loan Related to this point was the idea of dividing data storage between the landing area and an integrated warehouse. The landing area is the area where raw data arrives. Often, this will be unstructured data sitting in a Hadoop system. The data in the landing area is untransformed and cannot be easily analyzed. The data sitting in the integrated warehouse is transformed data that is ready to be analyzed. It can be used for analytics, visualizations, and dashboards; and is accessible to most company data scientists and business users. This warehouse can mix Hadoop and relational database data, as referenced in the first paragraph about logical data warehouses