DevOpsDreams

Listen to this article AI-narrated listening version - 10 min 06 sec. Code is explained; complete examples remain below. Your browser does not support audio playback. Open the MP3 . Read the narration transcript - Open audio Designing a three-tier AWS application that survives an Availability Zone or Region failure. Explore the architecture diagram, service choices, database consistency, identity recovery, and the trade-offs behind each decision. TL;DR A highly available three-tier AWS application can combine CloudFront and private S3 buckets for presentation, Global Accelerator and ECS Fargate for regional application services, and Aurora DSQL for strongly consistent writes across two Regions. Each application Region spans three Availability Zones and maintains failover capacity. A third Region acts as the DSQL witness, rather than another application deployment. Cognito multi-Region replication improves identity recovery but has feature limitations. This design explains regional ro...

Listen to this article AI-narrated article - 8 min 06 sec. Prose is preserved; code and diagram cues refer to the examples below. Your browser does not support audio playback. Open the MP3 . Read the narration transcript - Open audio Kubernetes 1.37 improves autoscaling, memory management and workload identity. Here are the changes worth understanding and the EKS checks to make before upgrading. TL;DR Kubernetes 1.37 Garhwal introduces practical improvements for production clusters. HPA scale-to-zero can remove idle workers, but requires metrics that survive absent Pods. Memory QoS is beta and enabled, while protection and throttling still need explicit kubelet settings. Stable Pod certificates require a configured signer and application reload support. Control-plane streaming reduces temporary read-buffer pressure, and Metrics API adoption must preserve HPA compatibility. Amazon EKS now documents 1.37 availability. Before upgrading, review SELinux volume sharing, Auto Mode consolida...

Listen to this article AI-narrated article - 28 min 01 sec. Prose is preserved; code and diagram cues refer to the examples below. Your browser does not support audio playback. Open the MP3 . Read the narration transcript - Open audio Applying SRE Error Budgets to Services Running on EKS: SLIs, Burn Rates, and Release Decisions Build error budgets around user requests, not healthy pods. Implement Prometheus burn-rate alerts, diagnose EKS failure modes, and turn reliability evidence into release decisions. TL;DR Error budgets turn a service reliability objective into an explicit allowance for unsuccessful user events. For EKS workloads, measure requests at a defined user boundary, calculate good and total events consistently, and distinguish application failures from load balancer failures and missing telemetry. This guide implements request-based SLIs, rolling budget calculations, and multiwindow burn-rate alerts with Prometheus. It also explains latency budgets, low-traffic edge ca...