### 5.1 Performance Posture The infrastructure exhibits moderate scaling risk concentrated in three high-volume telemetry assets. The **columns_catalog** table holds 1.5 million rows representing 75% of total dataset volume, while **job_logs** (303,822 rows) and **llm_calls** (49,360 rows) capture time-series event data without time-based partitioning. Current storage patterns suggest the platform was optimized for initial ingestion rather than analytical query workloads—text-heavy columns like `columns_catalog.column_name` and `job_logs.message` lack search acceleration, creating latency risk as teams filter logs or search metadata at scale. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | job_logs | Partitioning | day(ts) | High | | columns_catalog.column_name | Text Search | keyword | High | | job_logs.message | Text Search | tokenized_text | High | Implementing day-based partitioning on **job_logs** will dramatically reduce scan overhead when operations teams troubleshoot recent pipeline failures or analyze execution trends within specific time windows. Adding keyword indexing to **columns_catalog.column_name** and tokenized text search to **job_logs.message** will accelerate the most common investigative queries—finding specific column references across datasets and filtering error messages by keyword—cutting response times from seconds to milliseconds and reducing compute costs for repetitive diagnostic workflows.