AppDynamics Basics to Performance Mastery

Application performance issues cause 60% of customer-impacting incidents, making proactive monitoring critical for production systems. This course equips operations engineers to build observable distributed applications through hands-on work with AppDynamics for automatic discovery, transaction analysis, and intelligent alerting. You'll diagnose performance bottlenecks using snapshot-driven troubleshooting, eliminate alert fatigue by configuring baseline-driven health rules integrated with IT operations tools, and democratize observability through executive dashboards with automated reporting. This course eliminates the hours spent on manual log analysis across microservices. With hands-on labs, you'll simulate production scenarios including checkout slowdowns, database N+1 query patterns, and alert integration workflows. This course is designed for operations engineers, DevOps practitioners, SREs, application developers, and anyone responsible for monitoring, supporting, or optimizing production applications. If you work with distributed systems or want to strengthen your observability and incident-response skills, you’re in the right place. A basic understanding of web applications, APIs, and how backend services communicate will help you move quickly through the course. Familiarity with command-line tools and general application architecture (tiers, services, databases) is useful but not required. By course completion, you'll identify performance bottlenecks within minutes, reduce alert noise by 50%+, configure multi-channel notifications for sub-5-minute incident response, and deploy enterprise-scale monitoring with RBAC governance

• Configure AppDynamics agents and analyze transaction snapshots
• Create health rules and integrate alerts to reduce MTTR
• Build custom dashboards and deploy at enterprise scale
• Simulate production scenarios and analyze performance

• Prior hands-on experience with core AppDynamics concepts
• Comfort applying main tools and methods independently