### 5.1 Performance Posture The boxing dataset exhibits strong structural health across five dimension tables (cleandata through cleandata_v5) containing between 115 and 127 fighter records each, with no detected foreign-key relationships or transactional fact tables. At this scale—under 650 total rows—query performance is not a material constraint; all tables can be scanned in milliseconds without indexing. The absence of validated joins suggests these are versioned snapshots of fighter profiles rather than a normalized relational schema, meaning queries analyzing career trajectories or win/loss patterns will operate on individual tables rather than cross-table aggregations. ### 5.2 Key Optimizations | Priority | Table | Optimization | Rationale | |----------|-------|--------------|-----------| | — | — | No high-priority optimizations recommended | Current data scale supports sub-second query response across all tables | No infrastructure investment is warranted at present volume. The dataset's small footprint and flat structure deliver adequate performance for exploratory analysis of fighter attributes, knockout ratios, and match outcomes. Should the platform expand to capture live bout results or integrate historical fight-by-fight records—growing the dataset by an order of magnitude—revisit indexing strategies on frequently filtered columns such as weight class, win/loss counts, or knockout percentages. Until then, infrastructure resources are better allocated to data enrichment (linking fighters to specific match events) than to premature optimization of query execution.