### 5.1 Performance Posture The platform's telemetry infrastructure shows strong operational readiness but faces scaling pressure on its two largest assets. The **columns_catalog** table (1.5M rows, 75% of total dataset volume) and **job_logs** (304K rows) together dominate storage and query load, yet neither is optimized for the access patterns telemetry workloads demand—time-series filtering on logs and text-based column discovery across the catalog. With 94% referential integrity and validated joins across analysis runs, LLM usage tracking, and dataset lineage, the relational foundation is sound, but query performance will degrade as telemetry volume grows without targeted indexing and partitioning. ### 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 daily partitioning on **job_logs.ts** will accelerate troubleshooting queries that filter recent execution windows, directly reducing mean-time-to-resolution for platform incidents. Adding keyword indexing to **columns_catalog.column_name** and tokenized text search to **job_logs.message** will transform user-facing column discovery and log search from full-table scans into sub-second operations—critical as the catalog approaches 2M entries and operations teams query logs hundreds of times daily during analysis runs.