AI-Driven Observability: Smarter Logs, Metrics & Anomaly Detection AI-Driven Observability: Smarter Logs, Metrics & Anomaly Detection Every engineer knows the pain: a flood of alerts, endless logs, and dashboards full of red spikes. Traditional monitoring drowns us in data but starves us of insight. This is where AI changes the game — making observability not just bigger, but smarter. 🔍 Why Observability Has Outgrown Humans Modern software is distributed, ephemeral, and global. A single user request might pass through 300+ microservices, dozens of APIs, and multiple cloud regions. Observability — the ability to understand system health from external outputs — is no longer optional. But here’s the catch: the data is overwhelming . Gartner reports enterprises ingest 10+ terabytes of observability data per day [1] . This includes logs, metrics, traces...
Bugged But Happy is a software testing blog that shares real-world QA insights, ETL testing tips, SQL query guides, automation tools like Selenium, PySpark, and RPA, plus fun stories from the tester’s life. Whether you're a beginner or experienced tester, this blog helps you grow, debug smarter, and stay updated—while enjoying the journey. Because every bug teaches us something new!