### 5.1 Performance Posture The dataset is a single table, `semi_conductor_se`, with 422,729 rows. At that size it is comfortably manageable, and the 100% completeness score means queries won't be slowed by gaps or repair logic. Because the table holds time-stamped market records (a `date` column), the main scaling consideration is growth over time, not current speed. Every new trading period adds rows to the same table. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | semi_conductor_se | Partitioning | day(date) | High | Organizing `semi_conductor_se` by day means time-bounded questions, such as how semiconductor prices moved during a specific AI-driven rally, read only the relevant days rather than the full 422,729 rows. This should speed up trend and event-window analysis and reduce compute cost as history accumulates. The evidence supports no other tuning items, so no further changes are recommended at this stage.