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The Five Camps of Data Modeling (and Which One You're Stuck In)
Data modeling is fragmented into five distinct traditions, and failing to account for their conflicting requirements results in brittle, untrustworthy pipelines.
What: Data modeling has diverged into five distinct camps: relational, analytics, application, ML/AI, and knowledge/ontology. Mixing these paradigms, such as using an application-focused JSON schema for analytics or ML, creates friction and data quality failures.
Why it matters: Modern stacks require engineers to operate across multiple modeling traditions simultaneously, making it critical to identify which modeling philosophy is being applied at each stage of a data pipeline.
Original article
Data modeling grew up in five separate camps: relational, analytics, application, ML/AI, and knowledge/ontology, each solving different problems with real blind spots. One JSON product catalog can satisfy an application while breaking analytics, ML, and governance, producing brittle pipelines and lost dashboard trust. Modern stacks span all five camps at once, so shared literacy across them matters more than any single modeling tradition.
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