Data Modeling is as much about Data Engineering Architecture as it is about modeling the data only. Therefore besides the below links, many approaches and common architecture you can find in Data Engineering Architecture.

It’s getting more about language than really modeling, Shane Gibson says on Making Data Modeling Accessible. For example, a Data Scientist speaks Wide Tables, a Data engineer talks about facts and dimensions, etc., it’s what I call the different levels of data modeling.

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# Since Data Warehouse Toolkit Was Written

](#since-data-warehouse-toolkit-was-written)

Since Ralph Kimball has written the state-of-the-art book for Data Modeling called The Data Warehouse 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 Key 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-statement.
• 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.

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# How Does it Change with AI (-Agents)?

](#how-does-it-change-with-ai--agents)

I wrote a full article at Data Modeling for the Agentic Era: Semantics, Speed, and Stewardship. But also, with Vibe Coding it changes even more. I think, that a existing strong framework, strong Data Engineering Architecture matter more than ever.

Based on these, a AI Agents can produce more valuable content. See more at Vibe Coding.

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# Further Read

](#further-read)

• Data Modeling for the Agentic Era: Semantics, Speed, and Stewardship | ssp.sh
• Data Engineering, the future of Data Warehousing? | ssp.sh
• Babies and bathwater- Is Kimball still relevant.

Origin: Data Engineering, the future of Data Warehousing? | ssp.sh
References: Education is changing ETL is changing, Data Modeling
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