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Data Model Template for Engineering Teams
A data model template for designing entities, relationships, attributes, and constraints that support product features, reporting needs, and future...
Updated 2026-03-05
Data Model
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Frequently Asked Questions
Should product managers participate in data modeling?+
Yes, but at the conceptual and logical levels, not the physical level. PMs should define what entities exist, what attributes matter, and what business rules apply. Engineers handle the physical implementation (index types, storage engines, partitioning). The most productive data modeling sessions have both PM and engineering in the room, because PMs know the business rules and engineers know the technical constraints.
How do I decide between storing data as a separate entity or as a JSON field?+
If you need to query, filter, or sort by values within the data, make it a separate entity with proper columns. If the data is opaque to the application (metadata, preferences, configuration blobs), JSON is appropriate. If you are unsure, start with a JSON field and promote it to a proper entity when query patterns emerge. The [schema design template](/templates/schema-design-template) covers this tradeoff in more detail.
When should I use soft deletes vs. hard deletes?+
Use soft deletes when users might want to restore deleted items, when you need audit trails, or when downstream systems depend on the record existing. Use hard deletes for junction tables, session data, and truly ephemeral records. Soft deletes add complexity: every query needs a `WHERE deleted_at IS NULL` filter. Partial indexes help with performance.
How do I model features that have customizable fields?+
Three common approaches: (1) Entity-Attribute-Value tables (flexible but slow to query), (2) JSON columns (good for PostgreSQL with JSONB, moderate query support), (3) dedicated columns with a schema migration for each new field (rigid but fastest to query). For user-facing customization (custom fields on a CRM), JSON or EAV works. For internal product fields, dedicated columns are almost always better.
How far ahead should I plan the data model for future features?+
Plan one iteration ahead, not three. Over-engineering a data model for hypothetical future features creates unnecessary complexity. If you know a feature is coming in the next quarter, model for it now. If it might come in a year, note it in the "Evolution and Migration" section and revisit when it is confirmed. Adding a column later is cheaper than maintaining unused abstraction today. ---
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