Since Ralph Kimball has written the state-of-the-art book for Data Modeling called The Datawarehouse Toolkit - Ralph Kimball, data modeling is changing.

Especially with newer Data Engineering Approaches, tools land the landscape has drastically changed (see RW The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape).

Essentially, you can’t change ETL without modeling differently. Here are a few points that have been changed and will further change:

Further Denormalization for performance gains is mostly compensated with faster database engines or cloud solutions.Maintaining surrogate keys in dimensions can be tricky and not human-friendly as we prefer business keys.With the popularity of document storage and cheap blobs in cloud storage, it is becoming easier to create and develop database schemas dynamically without writing DML-statements.Systematically snapshotting dimensions compared to handling complex and maybe contra-intuitive Slowly Changing Dimension (Type 2) is a way to simplify track changes in a DWH. Is it also easy and relatively cheap to denormalize dimension attributes directly on the fact table to keep important information at the moment of the transaction?Conformed dimensions and conformance, in general, are extremely important in nowadays Data Warehouses and data environments. But to be more collaborative and work on the same objects it is a necessary trade-off to loosen it up.Not only are more working on the same project within data warehousing, but also more people from business and other departments getting more data-savvy than ever before. In that sense data needs to get more real-time rather than batch processing and precompute calculations, this can be done more ad-hoc with new fast technologies like Spark that ran complex jobs ad-hoc and on-demand.

See more on Babies and bathwater- Is Kimball still relevant.

References: Education is changing ETL is changing