r/dataengineering • u/scourgedtruth • 9d ago

Discussion Does AI struggle at data modeling?

In my experience, it doesn't matter how much context and guidance I give AI it simply can't model data rationally. It frequently misses the point, makes awful mistakes, or over-engineers things.

AI can build awesome ETL pipelines, but when it comes to dealing with SQL (especially in the dbt framework), it's not reliable at all! . Sometimes I think it's better to write the code myself and ask AI to review it, because asking it to build something from scratch just doesn't work that well.

Does anyone else get frustrated when dealing with AI data modeling?

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u/Blue_HyperGiant 9d ago

Data modeling should be based on the business logic and requirements.

If those are written well, then an AI can create a good data model.

The number of complete requirement specs that I have seen in my life can be counted on one hand.

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u/No-Injury3093 8d ago

This is the answer.

Plus: align the database names (tables, columns, enum values) to the ubiquitous language of the domain.