When starting a new application and designing the data model — good schema design is 10x harder to fix retroactively.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Databases & Data Engineering - Use case: Database Schema Designer - Source task: - Design a relational database schema for {{describe_the_domain}}. Include: - 1. all tables with column names, types, constraints (PK, FK, NOT NULL, UNIQUE) - 2. normalisation decisions : explain any deliberate denormalisation - 3. indexing strategy for common query patterns - 4. audit trail design (created_at, updated_at, soft delete pattern) - 5. SQL DDL statements to create the full schema # Goal Complete relational schema with DDL, normalisation rationale, index strategy, and audit trail implementation. # Constraints - Produce a complete, usable first draft in one response. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Complete relational schema with DDL, normalisation rationale, index strategy, and audit trail implementation.
{{double-curly}} with your real context.When starting a new application and designing the data model — good schema design is 10x harder to fix retroactively.
Add created_at and updated_at to every table from day one — you will always eventually need them and they are expensive to add retroactively.
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