This paper reveals a practical, data-first strategy for document database design, highlighting critical anti-patterns.
Relational database design is a well-understood process enabled by a combination of database theory (e.g., normal forms) as well as conceptual modeling (e.g., ER-based design). In contrast, database design for NoSQL databases, notably document databases, is often approached in a much more ad hoc manner. It is frequently driven by application details and physical considerations that muddy the design process in ways all too reminiscent of the pre-relational database era. In this paper, we argue for a return to sanity - for a logical, data-first, conceptually grounded approach to document database design. We explain how such an approach can work, yielding a clean, query-friendly document database design. We also highlight a collection of document (JSON) anti-patterns to avoid. The process and the anti-patterns both stem from the authors' experiences in current and past lives when dealing with a wide variety of JSON document data from commercial applications, government applications, and university research applications.
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Carey et al. (2025) studied this question.
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