OBT (One Big Table) vs Star Schema for Data Warehousing

I am trying to prepare myself for some interviews at a FAANG, while coming from a much smaller company. Part of the interview process involves data modeling and ETL design. Kimball's "The Data Warehouse Toolkit" describes a dimensional model that represents a company, like Target or Walmart, retail sales and its associated dimensions. However, Kimball seems to suggest that although dimensions (date, store, customer, product, etc) are highly denormalized, fact tables are normalized with FKs to their related dimensions. I have heard companies like mine using OBT or the "One Big Table" approach to make analysts lives easier and increase query performance by removing the need for JOINs. Also with storage being so cheap these days, and most modern DWs (Redshift, BiqQuery, etc) using a columnar based architecture, I would think we could safely throw out concerns regarding the extra redundancy (of having the same data appear twice in two tables), but is it possible that when architecting for huge applications that redundancy becomes an issue? What is the correct way of designing a data warehouse for each use case?