Best practices for optimizing Kubernetes applications on AWS
summary
Covers concrete practices: run Dynatrace OneAgent plus OpenTelemetry and CloudWatch/X-Ray to collect traces, metrics and logs; use the Davis AI causal analysis to find root cause across services; enforce SLO-driven CI checks that automatically approve or rollback releases; surface runtime third‑party library usage to prioritize vulnerabilities (example: a financial customer spent 32 hours manually evaluating a release before automating that check). Describes automated rollback, runtime vulnerability detection and tracing‑enriched logs — all implemented and used in production.
AI-written summary · about
chapters (9)
- 0:00Introduction and speakers
- 2:19Kubernetes complexity
- 9:07Observability platform
- 13:08Performance monitoring
- 21:29AI for analytics
- 25:25SLOs and automation
- 33:44Reliability in production
- 36:59Security and DevSecOps
- 55:00Key takeaways
related talks
56mFull-stack observability and application monitoring with
35mThriving With Kubernetes On-Call: Best Practices & Lessons Learned
30mPragmatic SRE for Kubernetes in the Cloud
34mOptimizing operations & container management
42mMastering Kubernetes: Challenges, Strategies, and Best Practices
32mOptimizing Application Performance on Kubernetes
53mImplementing Kubernetes on with confidence
37mkubernetes on AWS gone wild