Hybrid Cloud Configuration

🔄 Bridging On-Prem & Cloud: Running Stateful Workloads with OpenEBS & EKS Hybrid Nodes Excited to share my latest technical deep-dive on implementing hybrid cloud architecture for stateful workloads! Key highlights: • Seamlessly manage persistent storage for containerized applications on-premises • Leverage OpenEBS with Amazon EKS Hybrid Nodes • Maintain cloud-native practices while meeting data locality requirements • Achieve operational consistency across hybrid environments 💡 Why it matters: Organizations often struggle to maintain cloud-native practices while running stateful workloads on-premises. This solution bridges that gap, offering the best of both worlds. In my detailed guide, I walk through: ✅ OpenEBS implementation ✅ Local Persistent Volumes setup ✅ Dynamic provisioning configuration Read the full article here: https://lnkd.in/eRZ2K_NJ #CloudNative #Kubernetes #EKS #Hybrid #Storage #DevOps #AWS

🚀 Hybrid Cloud Done Right: Amazon EKS + VMware Cloud on AWS This architecture brings together the best of both worlds — cloud-native agility via Amazon EKS and legacy workloads hosted in VMware Cloud on AWS — to create a seamless hybrid application platform. Here's a breakdown of how it works: 🔹 1. Elastic Network Interface enables fast, secure connectivity between EKS pods and VMware-based database workloads. 🔹 2. Private Subnet Deployment keeps all EKS resources isolated and secure. 🔹 3. Managed Amazon EKS Cluster runs microservices (service-ui, service-app) and pods with full Kubernetes orchestration. 🔹 4. VMware Cloud on AWS hosts critical database workloads using the NSX-T overlay network and Tier-0 router. 🔹 5. Network Load Balancer exposes services through Kubernetes Ingress for external access. 🔹 6. Amazon Route 53 routes user traffic efficiently to your load balancer and backend services. 🔹 7-11. DevOps Automation Stack AWS CodePipeline automates deployment AWS CodeCommit stores code CodeBuild compiles and tests Amazon ECR hosts Docker images EKS auto-deploys updated containers seamlessly ✅ This architecture supports hybrid deployment models, modern DevOps, and secure service-to-database connectivity — all without refactoring legacy databases. 📣 If you're looking to modernize without ripping and replacing everything, this is the blueprint to start from. #HybridCloud #EKS #VMwareCloudOnAWS #Kubernetes #DevOps #CloudArchitecture #AWS #CloudNative #ModernInfrastructure #Route53 #CodePipeline #CodeBuild #GitOps #LinkedInTech #CloudComputing

Hybrid Cloud Is Becoming the Default Enterprise AI Platform For years, “cloud-first” was treated like a universal truth. But as AI moves from proofs of concept to production, enterprise platforms are being stress-tested in ways traditional application stacks never were. From my perspective, hybrid cloud isn’t a compromise anymore—it’s quickly becoming the most practical operating model for modern enterprises. AI changes the cost conversation because it introduces workloads that are compute-hungry and often always-on. When you’re training models, fine-tuning continuously, or running inference at scale across the business, the economics can shift fast. Elasticity is still valuable, but predictability becomes just as important—especially when leadership wants reliable unit costs and fewer billing surprises. Latency also stops being an optimization goal and becomes a hard requirement. There are plenty of use cases where you simply can’t afford the round trip to a distant region and back. When decisions need to happen in milliseconds, placing inference closer to users, devices, and operations is less about preference and more about making the system viable. Then there’s data gravity, governance, and sovereignty. Real-world enterprises don’t get to pretend that sensitive data, jurisdictional rules, and internal controls are optional. Many organizations will keep critical datasets and portions of the AI pipeline close to where the data is created and governed, because that’s often the simplest path to compliance and risk reduction. What’s emerging is a practical three-tier model that I expect to become the norm: public cloud for speed, experimentation, and burst capacity; on-prem for consistent production workloads that benefit from tight control and predictable economics; and edge for real-time inference where latency and availability are non-negotiable. The winning strategy isn’t choosing one environment—it’s placing workloads where they run best, technically and financially. If you’re shaping your enterprise platform strategy for 2026, start with this: are you optimizing around an ideology, or around workload reality? #HybridCloud #EnterprisePlatforms #AIInfrastructure #CloudComputing #EdgeComputing #PlatformEngineering #FinOps #DataGovernance #EnterpriseIT #DigitalTransformation

Hybrid Cloud vs Multi-cloud 👇 ✅ Hybrid Cloud When thinking about a hybrid model, this means you want to incorporate both on-prem and in the cloud. And despite what the internet tells us, on-prem is still very popular and very-much needed for some organizations. The goal with hybrid would be to run some functions locally (maybe for compliance or bandwidth purposes) while other functionality/app stacks run in the cloud. A good thing to do here as well is make your services burstable. For example, if you're running out of resources in your on-prem datacenter, you can "burst" those resources to the cloud. Some noticeable tools/platforms in this arena are: 1. Azure Local (used to be Azure Stack HCI). 2. Google Anthos 3. EKS Anywhere 4. AWS Outposts ✅ Multi-cloud When thinking about multi-cloud, it's when you use more than one cloud to run your functionality and application stacks. A good example is if you want to use Azure Active Directory (AD)/Entra for authentication and authorization for your Google Kubernetes Engine (GKE) cluster. One big thing to point out here is to understand what exactly you want to do with multi-cloud. For example, I have clients that ask to build multi-cloud, but when we dive into the needs together, it turns out they want redundancy, which could be implemented in one cloud with multi-region instead of multi-cloud. The most important factor to keep in mind here, as with all technology implementations, is to ensure you know what you actually need prior to implementing anything. As with any good SDLC process, come up with, architect, and plan first. #cloud #kubernetes #devops