SQL Design Patterns vs Anti-Patterns: When to Move Logic to the DB vs the App

SQL debates are heating up: should you push logic into the database or keep it in the app? Patterns promise clarity; anti-patterns warn against missteps. The landscape is sketched in the discussion around SQL design and common pitfalls ^[1][2].

Pattern Play — From SQL Design Patterns for Data Engineering, teams chase repeatable, maintainable approaches that keep data workflows in check. When projects face heavy, clumsy conditional logic, a practical nudge from SQL Anti-Patterns is to consider a dimension table or view sourced from the landed table instead of sprawling CASE WHENs ^[2]. That tweak often clarifies the data story and unitizes the logic you actually need to reuse ^[2].

Anti-pattern Warnings — The anti-pattern discussions aren’t a doomscroll; they’re a map. A core lesson is to avoid ad-hoc, unparameterized queries that invite misreads or, worse, injections. The Nim case makes this painfully concrete: parameterization failures in Nim's db_Postgres module for PostgreSQL show how neglecting bindings can open the door to SQL injection risks ^[3]. Bold emphasis on secure, parameterized queries is not optional here ^[3].

Security & Correctness Takeaways — The thread emphasizes two non-negotiables: treat parameterization as a guardrail for correctness, and favor patterns that keep data logic readable and audit-friendly. When in doubt, move the right, stable logic into a dimension-like construct or view, and keep raw, user-supplied input safely bound ^[2][3].

Bottom line: pattern-minded design and strict parameterization aren’t rival playbooks—they’re complementary tools for safer, faster data work ^[1][3].

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