Automating Deployments for Global Streaming Provider with AWS

CloudWatch shows healthy CPU and memory but your application response times are climbing. Native AWS monitoring tracks infrastructure without correlating to how requests move through Lambda, RDS, and microservices. Applications Manager delivers full-stack monitoring with unified views, dependency mapping, and proactive alerting to reduce MTTR before users notice issues. Download a free 30-day trial of ManageEngine Applications Manager by visiting https://bit.ly/4uDZfPH #AWSMonitoring #CloudWatch #DevOps

Working with real-time streaming systems recently reminded me how different they are from typical stateless APIs. When running streaming servers behind a load balancer, session management becomes critical. Unlike standard REST services, these systems often maintain: • long-lived connections • server-side session state • in-memory subscriptions If requests from the same client are routed to different instances, that state can be lost. One practical solution is load balancer session affinity (stickiness). In AWS, this can be implemented using an Application Load Balancer with cookie-based stickiness, allowing client sessions to remain consistently routed to the same backend instance. This makes it possible to combine: • stable long-lived connections • horizontal scaling of containers • automatic container replacement by the orchestrator I’ve been testing this approach in a containerised environment to ensure that: • unhealthy containers are replaced automatically • clients reconnect cleanly after failover • subscriptions can be re-established without disruption It’s a good reminder that not all distributed systems behave like stateless APIs. Real-time workloads require thinking much more carefully about: • session affinity • connection lifecycle • reconnection strategies Small infrastructure details like these can have a surprisingly large impact on the reliability of distributed systems! Curious how others handle session affinity for real-time workloads in container environments. #AWS #DistributedSystems #CloudArchitecture #DevOps #PlatformEngineering #ECS #Fargate #Docker

Designing a reliable Kubernetes setup starts with understanding how traffic enters and moves inside the cluster. 🔹 ClusterIP – Default service type for internal pod-to-pod communication. 🔹 NodePort – Exposes services on a static port across nodes for basic external access. 🔹 LoadBalancer – Cloud-managed external load balancing for production workloads. 🔹 Ingress – Layer 7 routing with host and path-based rules, enabling multiple services behind a single entry point. Choosing the right approach depends on architecture, security, scalability, and environment (cloud vs on-prem). What’s your go-to setup for production workloads? #Kubernetes #DevOps #CloudEngineering #K8s #Containers

Most teams choose EKS because it's easier — not because it's right for them. After working with both in production, the difference isn't just managed vs unmanaged. It's about where you want to spend your engineering hours. In enterprise environments, the Kubernetes decision affects your team's velocity, your cloud bill, and your 3 am on-call experience for the next 3 years. Getting it wrong is expensive. Here's the framework I use: Choose EKS when: Your team is small and you cannot afford a dedicated platform engineer managing etcd, control plane upgrades, and API server availability. AWS handles all of that. You pay for it, but you buy back engineering time. You need deep AWS integration out of the box — IAM roles for service accounts, ALB Ingress Controller, EBS/EFS CSI drivers. These work natively. On self-managed, you wire them yourself. Choose self-managed when: You need full control over control plane configuration — custom admission webhooks, specific API server flags, or air-gapped environments where EKS simply cannot reach AWS endpoints. Your scale justifies a platform team. At 500+ nodes across multiple clusters, the EKS per-cluster cost and the constraints around control plane access start to matter more than convenience. The hidden cost nobody talks about: EKS is not "zero ops". You still manage node groups, cluster add-on versions, networking (VPC CNI), and upgrade windows. Teams that choose EKS expecting zero Kubernetes expertise still get paged at 3am — just with fewer levers to pull. The real decision framework: Do you have a dedicated platform team? No → EKS Yes → How much control plane customisation do you need? Low → EKS High → Self-managed (kubeadm / kops / Talos) The best Kubernetes setup is the one your team can operate confidently at 2am without reading documentation. What does your team run in production — and what would you choose if you started fresh today? #DevOps #Kubernetes #EKS #CloudInfrastructure #PlatformEngineering #AWS #SRE

⚙️ Kubernetes Lesson: When Scaling Doesn’t Actually Scale Faced a situation where: 1. Increased replicas from 2 → 6 2. Deployment updated successfully 3. Pods were running fine But…Application performance didn’t improve, Requests were still slow. 🔍 What I checked next: * Pods status → Running ✅ * CPU/Memory → Under limits ✅ * No crash loops ✅ Still no improvement… 🤔 💥 Actual issue:- Traffic was NOT getting distributed properly Reason :- Service was configured correctly, but application had sticky sessions enabled. So same users kept hitting same pod, load was not balanced. 🛠️ Fix: ✔ Disabled sticky sessions ✔ Verified load distribution across pods ✔ Added metrics to track per-pod traffic 💭 Key Takeaway: Scaling pods doesn’t guarantee scaling performance. You also need: 1. Proper load balancing 2. Stateless application design 3. Session management strategy 🚀 Kubernetes scales containers… not bad architecture. #Kubernetes #DevOps #Scaling #Cloud #Microservices #SRE #Performance #Debugging 🔥

Designing resilient, scalable traffic management and network constructs within Kubernetes, especially on EKS, demands architectural precision beyond basic YAML definitions ⚙️. The journey from standard Ingress to the power of Gateway API introduces fascinating challenges and opportunities. I've been deep-diving into leveraging AWS ALB and NLB for various Ingress patterns, contrasting their operational nuances and scale implications. Crucially, managing IP address exhaustion in large EKS clusters led me to double down on Prefix Delegation strategies. My latest work explores the advanced capabilities of the AWS Load Balancer Controller, from intricate ALB annotations for fine-grained routing and SSL management, to integrating NLB with both Nginx Ingress and the more declarative Envoy Gateway API. This isn't just about deploying; it's about optimizing network fabric for high-density workloads, ensuring efficient IP utilization, and setting up intelligent routing policies for multi-service environments. The architectural 'why' behind each choice is paramount for maintaining performance and operational stability at scale 🚀. Full architectural breakdown and implementation details are now live at atlas.ahmadraza.in #PlatformEngineering #DevOps #EKS #Kubernetes #CloudArchitecture #DistributedSystems #GatewayAPI #AWS

🚀 Stop exposing every microservice with its own Load Balancer — there’s a better and more cost-effective way. 💸 Instead, consider using an Ingress Controller as a single entry point to manage all incoming traffic and route it internally based on defined rules. On AWS (EKS), pairing this with the AWS Load Balancer Controller enhances the process: - One ALB can serve multiple services - Easy path-based routing like /api, /login, /dashboard - Built-in SSL handling via ACM - Seamless integration with AWS networking and security This approach stands out because it: - Reduces infrastructure costs significantly - Centralizes and strengthens security - Keeps your architecture clean and production-ready - Scales effortlessly as your system grows #AWS #EKS #Kubernetes #DevOps #CloudArchitecture #IngressController #TechTips

As part of HR-ON’s scaling journey, we’ve been re-architecting our infrastructure to ensure our services stay fast and reliable as our user base grows. To support our growth, we’ve prioritized shifting to a container-first architecture and adopting IaC to ensure our systems scale seamlessly. We’ve ultimately decided to move forward with ECS over EKS, avoiding premature optimization today. Having worked with Kubernetes for over 5 years in previous roles, choosing ECS wasn’t an easy decision. But as a small team, our priority was simple: to run containers efficiently and scale without unnecessary complexity or the burden of heavy upskilling for the team. Kubernetes is often considered vendor-neutral in theory. In practice, running EKS still ties you closely to the AWS ecosystem through load balancers, networking, storage, and more. When you take that into account, the “neutrality” argument becomes less compelling for many use cases. If your workloads don’t require advanced features like complex affinity rules or complex service mesh overhead, ECS (especially with Fargate) is a powerful alternative. It removes the burden of cluster management, offers fine-grained resource allocation, and simplifies operations significantly. Combined with a solid IaC setup, this approach allows us to fully manage our infrastructure while keeping things lean and efficient. Sometimes, the best architectural decision isn’t the most popular one. It’s the one that fits your context, your team, and your scale. #CloudEngineering #PlatformEngineering #AWS #Terraform