Here is the recommended approach for How to handle missing data and outliers in large analytical datasets without biasing metrics?:
1. Identify the Core Bottleneck: Check if the bottleneck is caused by unindexed database queries, missing execution timeouts, or payload formatting issues.
2. Implement Guardrails & Fallbacks: Always add input validation at the boundary layer and set explicit timeouts on third-party service calls.
```bash
# Verify system status
php artisan --version
```
3. Keep Infrastructure Simple: Avoid adding external infrastructure until your current framework setup (PostgreSQL, Redis, or job queues) hits clear limits.
Best Practice: Monitor query response times and error rates continuously using APM tools to catch degradations early.