Hybrid Cloud & Platform Engineer
Hybrid Cloud & Platform Engineer with 13+ years of experience, spanning 8 years in enterprise software engineering and 5+ years in cloud-native & multi-cloud DevOps architecture. Specializes in designing scalable, secure, and high-availability hybrid cloud platforms through infrastructure automation (IaC), container orchestration (Docker, Kubernetes, Swarm) and application-aware system design that bridges the gap between development and infrastructure.
- AWS
- GCP
- OCI
- Azure (Supporting)
- Bare Metal (KVM / QEMU)
- Kubernetes
- Docker Swarm
- EKS
- GKE
- K3s
- Terraform
- Ansible
- Kustomize
- Github Actions
- Gitlab
- Jenkins
- Argo CD
- Cloudflare Zero Trust
- Zerotier
- Site-to-Site VPN
- Prometheus | Grafana | Loki
- ELK
- C#, Golang, Python
- Bash
Real-world benchmarks from production migration projects. Empirical data collected across multiple infrastructure configurations โ bare metal Kubernetes, managed cloud, and distributed storage.
Head-to-head benchmark between self-managed CloudNativePG on bare metal Kubernetes and AWS RDS / Aurora โ same workload, same VM class, progressive tuning iterations.
Quantifying the cost of AOF + RDB persistence across local NVMe and distributed Longhorn storage. Defines the architectural boundary for durability vs throughput.
An e-commerce platform migrating from managed cloud to on-premises Kubernetes raised a
critical question: can self-managed PostgreSQL match managed service performance after tuning?
This benchmark compares a single CNPG instance on bare metal against
single-instance RDS Standard and Aurora (t3.large) โ same 2 vCPU / 8 GB RAM
allocation, same region (ap-southeast-3), no read replicas or Multi-AZ.
โ Note: RDS Standard uses a t3.large (burstable) instance.
Our ~50 minute total benchmark ran within the burst window โ results reflect peak burst performance.
In production workloads running continuously 24/7, performance will drop once CPU credits exhaust
(t3.large baseline CPU = 30%). For sustained production comparison, consider
m6i.large or m7g.large (non-burstable).
3-layer tuning stack: KVM hypervisor (NUMA strict, vCPU pin, locked memory) โ VM OS (HugePages pre-alloc, perf governor) โ Kubernetes (Guaranteed QoS, hugepages-2Mi resource). Each Longhorn replica adds ~3.5ms write latency. NUMA pinning eliminates cross-NUMA penalty โ write latency at 1 client drops from 7.02ms โ 1.81ms (-74%). * Latency at 1 client.
RW bars show write-only avg โ the most relevant metric for OLTP. Read avg is similar across all bare metal configs. Overall avg includes RO+RW+TPC-B and is dominated by read volume. โ RDS Standard (t3.large) is burstable โ its 4,826 overall avg reflects burst window performance (~50 min benchmark). In 24/7 production, performance drops once CPU credits exhaust. CNPG Tuning 2's 3,351 is consistent regardless of duration.
When designing the caching layer for a high-throughput production workload, the team needed to answer a hard question: can Redis AOF persistence coexist with distributed block storage? These benchmarks โ covering 9 configurations across local NVMe and Longhorn โ give a definitive architectural answer.
From cost optimization to hybrid cloud architecture โ let's build something that lasts.
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