A practical learning path and improvement experiments
Prerequisites: 13-lifecycle, 11-security, 10-scaling
14 / Improve a baseline with evidence
Scroll the diagram sideways for readable labels.
Learning objective and mental model synthesis
Treat the platform as a set of contracts: desired state, traffic delivery, permissions, persistence, capacity and recovery. Improve one weak contract at a time. This is a synthesis of the preceding mechanisms; the right next experiment depends on your workload and operating responsibility.
[S02] [S09] [S22]Lab 1: local fundamentals synthesis
In a disposable test environment, deploy a small HTTP service, a Service and the supplied manifest specimen after replacing the image and implementing its endpoints. Inspect ownership and labels, replace one Pod and observe how the controller restores the count. Record time until useful traffic resumes. Do not run the specimen unchanged against production.
[S02] [S04] [S05]Lab 2: capacity and failures synthesis
Increase load gradually, compare resource requests with measured demand, then inspect the HPA metric and node provisioning path. Introduce one dependency failure and one failed rollout in the test environment. A good experiment specifies demand, duration, acceptable latency/error behavior and what evidence would reject the hypothesis.
[S08] [S26] [S10]Lab 3: EKS integration synthesis
Choose managed nodes or Auto Mode according to the stated responsibility matrix. Verify private administration access, identity resolution, routing, storage compatibility and subnet headroom. Test an allowed and denied AWS action. Costs accrue for an EKS environment; plan resource cleanup as part of the exercise.
[S19] [S17] [S15] [S23]Lab 4: recovery and next topics synthesis
Restore data and workload configuration into an isolated compatible environment; validate records and measure RPO/RTO against your chosen objectives. Then study workload-specific topics: operators for data services, progressive delivery, policy admission, multi-cluster recovery or queue-based scaling. Add those only when the baseline measurements expose a real need.
[S06] [S22] [S27]Evidence limits open-question
Examples were structurally reviewed and parsed where supplied as YAML; they were not deployed to an AWS account or live cluster. No load-test, recovery-time or savings result was measured. Official sources were retrieved on 2026-10-10; living docs, region support, feature constraints and pricing need rechecking before implementation.
Check yourself: What would make the proposed HPA improvement fail its evaluation?
If more replicas do not improve useful throughput or meet latency/error objectives, or if startup lag and dependency saturation negate the expected benefit, the experiment rejects or narrows the hypothesis.