I was expecting a discussion of the foundations of data modelling: star schema vs snowflake schema data models vs one big table. The benefits of 3NF vs when you have to denormalize.
This underlying choice of data model actually does define your destiny. What I think the author was thinking of is domain modelling and correct entity identification, which is also important. It's a layered approach - and if you ignore the foundations (the actual data model), you hit limitations higher up.
For example, in real-time AI systems, you might want users to provide a single value (like an order number) to retrieve precomputed features for a model. With Snowflake Schema data models, it works. But for Star Schema data models, you have to provide entity IDs for all tables containing precomputed features - which leads to big problems (the need for a mapping table, a new pipeline, and higher latency).
I prefer your terminology. That being said, domain modelling (what the article describes) comes first, hence is more foundational and important than data modelling.
This underlying choice of data model actually does define your destiny. What I think the author was thinking of is domain modelling and correct entity identification, which is also important. It's a layered approach - and if you ignore the foundations (the actual data model), you hit limitations higher up.
For example, in real-time AI systems, you might want users to provide a single value (like an order number) to retrieve precomputed features for a model. With Snowflake Schema data models, it works. But for Star Schema data models, you have to provide entity IDs for all tables containing precomputed features - which leads to big problems (the need for a mapping table, a new pipeline, and higher latency).
Reference: https://www.hopsworks.ai/post/the-journey-from-star-schema-t...