Emerging Trends in Kubernetes Implementation

At the moment I’m seeing a LOT of posts about the doom and gloom of Ingress on Kubernetes. Some of it is coming from the right place, others being misguided. This is typical of what happens when one story starts and then gets cloned/mimicked by others without the right context. There are two things to separate: ▶ Ingress - The Kubernetes API resource (networking.k8s.io/v1) ▶ Ingress NGINX - The community ingress controller implementation Most of the context is about Ingress NGINX, not about Kubernetes deprecating Ingress as an API. To be clear: the Kubernetes Ingress API is GA and there are no announced plans to deprecate it. Gateway API is its successor in terms of design, but Ingress as a Kubernetes resource is still very much alive and supported. Ingress NGINX (the controller) is in best-effort maintenance and is scheduled for retirement in March 2026. After that: no new releases, no bugfixes, no security patches. The recommended direction of travel is towards Gateway API and/or other actively maintained ingress controllers. On the NGINX side, their focus is moving (rightly so) towards NGINX Gateway Fabric, their implementation of the Gateway API using NGINX as the data plane. That’s where the innovation is happening. The move towards Gateway API is a positive one: ▶ It’s more robust and flexible. ▶ It bakes in role separation (infra/platform/app) by design. ▶ You get more powerful routing - headers, methods, advanced matching. ▶ You can take advantage of areas like traffic splitting and request mirroring (implementation-dependent, but commonly available). Working towards Gateway API support in your platform is a good step. My main highlight though is that distinction again: 'Ingress in Kubernetes vs Ingress NGINX as a controller'. Ingress NGINX will stop receiving development and security fixes but that does not mean "Ingress is dead". There are multiple Ingress controllers out there that continue to support the Ingress API - for example: https://lnkd.in/eEyg8Adf From a study and certification point of view, should you still learn Ingress? ABSOLUTELY. Modern Kubernetes exams (like the updated CKA) expect you to know how to use Ingress controllers and Ingress resources, and use the Gateway API to manage ingress traffic. The migration from Ingress to Gateway API is already a favourite pattern in hands-on / performance-based training and practice labs and it’s showing up in real exams too. If you truly understand both Ingress and Gateway API and can map one to the other, you’ll be in a great spot to tackle those tasks with confidence. Ingress isn’t going away - one very popular controller is. Learn both "Ingress" and "Gateway API", and you’ll be ahead of the curve.

I just spent the week researching Kubernetes security trends for 2025, and one stat stopped me cold: AKS clusters face probing attempts within 18 minutes of deployment. EKS? 28 minutes. Your cluster is under attack before you finish deploying it. The reality check: • 58% of organizations experienced a K8s security incident this year • 43% of environments remained vulnerable after IngressNightmare CVEs • Only 54% run supported Kubernetes versions (46% exposed to known CVEs) Security can no longer be an afterthought in cloud-native infrastructure. The community is responding with Open Source SecurityCon, zero trust architectures, and tools like Falco, Kubescape, and Kyverno gaining serious traction. Service mesh adoption is accelerating for mTLS and identity-based security. But here's the uncomfortable truth: we're deploying sophisticated AI/ML workloads on infrastructure where nearly half the clusters are running outdated versions. Platform engineers and security teams need to work together—not in silos. Security must be baked into the platform from day one, not bolted on after the breach. What's your team doing to close the gap between deployment speed and security posture? #Kubernetes #CloudSecurity #PlatformEngineering #DevSecOps #CloudNative

There's been a lot of buzz around Kubernetes 1.30, and for good reason. This release packs a serious punch, especially when it comes to security. But it's not all about keeping the bad guys out (although that's pretty darn important). This update also brings some exciting improvements for developers. ➡ Kubernetes 1.30 cracks down on unauthorized access to your precious secrets. KEPs like #2535 enforce stricter controls on container images, while #2799 reduces reliance on less secure service account tokens. This is a win for security and peace of mind! ➡ The new node log query feature simplifies administration by letting you access logs without needing direct system access. ➡ CEL integration for admission control opens the door for more granular and secure policies. This is a game-changer for organizations with complex security needs. ➡ #3141 prevents unauthorized volume mode conversion during volume restore, safeguarding data integrity. ➡ Faster SELinux label changes (#1710) translate to quicker container startups, especially for environments with tons of files. Beyond security, this update is also about making developers' lives easier. ➡ Go Workspaces for Kubernetes (#4402): Streamlines development workflows. ➡ Graceful Shutdowns with Sleep Action for PreStop Hooks (#3960): Say goodbye to data loss and incomplete transactions during pod termination. ➡ Container-Level Pod Autoscaling (#1610): Fine-tuned scaling for complex applications with varying resource demands. What Does This All Mean? 🔐 This release elevates security within Kubernetes. We might see specialized security roles emerge for crafting robust policies and leveraging granular control features. 💡 Improved developer experience combines with security features to create a perfect storm for DevSecOps adoption. 💪 Features like removal of deprecated plugins and emphasis on structured configuration push Kubernetes towards a more standardized and secure future. 📚 As Kubernetes security matures, so too will the skillsets needed by professionals. A deeper understanding of security concepts and these new features will be crucial. I am excited to see Kubernetes 1.30 propel the platform forward! Check out the @ARMO rundown here:[https://lnkd.in/duaj6PmE ] #Kubernetes #Security #DevOps #FutureofTech

Cloud Native 2025: If You’re Not Doing These 3 Things, Your Apps Are Already Dead “84% of cloud-native apps will fail production within 12 months—not due to code, but obsession with legacy Kubernetes hacks” (CNCF 2024 Report). The future isn’t containers. It’s what you do after deploying them. A healthtech startup scaled to 1M users using “best practice” microservices… until their $200k/month AWS bill exposed the truth: 300 zombie containers auto-scaling nothing. The fix? Switching to WebAssembly (Wasm) modules slashed runtime costs by 65% and cold starts to near-zero. 1️⃣ Serverless > Servers AWS Lambda now handles 90% of Netflix’s backend. Why? Millisecond billing beats idle VMs. Tool to Try: Cloudflare Workers with Wasm for edge-native logic. 2️⃣ AI-Driven DevEx GitHub Copilot writes 40% of infra code for companies like Brex. Trend: “Self-Healing” K8s (e.g., KubeArmor auto-blocks anomalous pod behavior). 3️⃣ Platform Engineering Mutiny Internal Developer Platforms (IDPs) like Humanitec let devs self-serve infra without drowning in YAML. Case Study: A bank reduced deployment tickets by 80% after letting teams spin up pre-approved envs via Slack. Actionable Steps: 1. Kill Zombie Orchestration Find idle K8s pods with PromQL 2. Adopt Observability-Driven Development Tools like OpenTelemetry auto-trace app flows across hybrid clouds. 3.Prep for WASM 2.0 Replace bloated sidecars with Fermyon Spin for lightweight, secure plugins. The Brutal Truth: “Cloud-native” isn’t about containers anymore. It’s about apps that adapt to chaos —spiking traffic, AI-driven attacks, and devs who hate ops. Still using Helm charts like it’s 2020? 👇 Tag a team that needs this wake-up call. #CloudNative #DevOps #Serverless #TechTrends #SoftwareEngineering

I'm a bit late to the game, but I'd like to share the top 5 trends I saw this year at #Kubecon, and after years of going to Kubecon, I see a clear trend... More clusters, More tools: Crossplane and ClusterAPI are leading the charge toward automating cluster management, moving away from manual setups to scalable, code-defined infrastructure, and addressing the complexities of managing multiple clusters. The big Boys (100+) cluster problem is starting to take off! GitOps is everywhere: The integration of Argo and Flux marks a significant shift towards GitOps, establishing it as the go-to strategy for Kubernetes deployment and management. Simply, everyone is doing it :) Rise of Developer Platforms: The popularity of platforms like #Backstage and the adoption of Internal Developer Platforms (IDPs) signal a growing focus on enhancing developer efficiency and experience, making it easier for developers to produce their best work. Dev are people, too! Everyone tries to do this, but few companies actually succeed. Networking and Security Priority: Cilium’s rise as a standard highlights the increasing importance of secure, observable networking within Kubernetes. This trend emphasizes the critical need for robust network management and security practices. It will be interesting to see what happens after the Cisco acquisition. Hybrid and Multi-Cloud Flexibility: The shift towards more clusters and the embrace of hybrid and multi-cloud strategies show organizations aiming for resilience and flexibility, leveraging the best of various cloud environments for their needs. If you are big, you should do this; if not, don’t :) To conclude, Kubernetes transitioned from being the new tech on the block to the infrastructure we (all) rely on, making him the new tool everyone loves to hate.

Kubernetes just got smarter about hardware — and that’s a big deal for AI. Dynamic Resource Allocation (DRA) that went GA in k8s 1.34 unlocks a new way to manage GPUs, FPGAs, and other specialized devices in Kubernetes. Instead of static allocation, DRA lets you define device classes and claims, so workloads get the exact resources they need — no more underutilization or rigid scheduling. Why it matters: 1. For GPU-intensive AI/ML workloads, DRA ensures fair sharing or dedicated allocation, improving performance and efficiency. 2. It simplifies scaling AI pipelines where multiple teams or models need controlled access to accelerators. 3. It future-proofs Kubernetes clusters for emerging workloads in generative AI, HPC, and data analytics. In our first two blog posts on the k8s DRA series, we break down: - Why DRA matters? - What DRA is and how it works - Roles of Cluster Admins and Workload Admins If you’re building or scaling AI workloads on Kubernetes, DRA is a must-know capability. 👉 https://lnkd.in/gEn5uwnS and https://lnkd.in/gVHKbjrx