DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
Mostrar más📈 Análisis del canal de Telegram DevOps&SRE Library
El canal DevOps&SRE Library (@devopslibrary) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 19 759 suscriptores, ocupando la posición 6 527 en la categoría Tecnologías y Aplicaciones y el puesto 33 399 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 19 759 suscriptores.
Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 137, y en las últimas 24 horas de 7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 14.01%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.00% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 768 visualizaciones. En el primer día suele acumular 1 382 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como kubernete, cluster, infrastructure, storage, configuration.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 28 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
Step-by-step guide to deploying a private Amazon EKS cluster with zero public API exposure, self-hosted OpenVPN access, kube-prometheus-stack monitoring, and Route 53 private DNS — all automated with Terraform.https://dev.to/aws-builders/building-a-production-grade-private-eks-cluster-with-openvpn-prometheus-grafana-419
Here we’ll walk through a small, concrete demo where a single hub control plane actively reconciles cert-manager.io/v1 Certificate resources across multiple downstream clusters, issues TLS Secrets, and does so safely from multiple controller replicas without relying on leader election.https://medium.com/@zach.dsmith/a-hub-style-multi-cluster-cert-manager-control-plane-d568ea334998
In this post, we'll take a polyglot microservice app from local source code to a production Kubernetes cluster with TLS, Auth0 login, and Stripe webhooks — all working end-to-end. No cloud staging environment. No Docker Compose. No YAML by hand.https://dev.to/jeffvincent/from-source-to-production-with-oauth-the-full-kindling-flow-1i73
A practical guide to packaging kong custom plugins as Helm charts, deploying them as ConfigMaps, and automating releases with GitHub Actionshttps://medium.com/@shambhand2020/installing-kong-gateway-custom-plugins-on-kubernetes-using-helm-charts-b6d6f3434c3a
In this article we explain how we built a new observability platform over the span of approximately two years to address a variety of problems.https://blog.ah.technology/observability-at-albert-heijn-d901f4caac8a
⏺ Эволюция инструментов: расскажем, почему старые подходы не работают и как выбрать современное решение. ⏺ Бизнес-драйверы: обсудим, как бизнес-задачи определяют тренды развития мониторинга. ⏺ Эпоха ИИ: покажем, как ИИ помогает автоматизировать процессы и анализировать данные. ⏺ «Астра Мониторинг»: продемонстрируем решение, которое позволяет не просто видеть, а предвидеть. Приходите, чтобы узнать, как сделать ваши ИТ-системы прозрачными, управляемыми и надежными.🔗 Зарегистрироваться на вебинар
How we eliminated crash loops and hundreds of errors and why “high availability” was making everything worse.https://medium.com/@j.aslanov94/why-your-grafana-is-slow-on-kubernetes-and-3-replicas-wont-fix-it-f375527de85a
A few years ago, Roland Huss and I wrote Kubernetes Patterns, a catalogue of repeatable solutions for building cloud-native applications on Kubernetes. The book covers patterns across six categories: foundational, behavioral, structural, configuration, security, and advanced, everything from health probes and init containers to controllers, operators, and elastic scaling. Recently, Roland and Daniele Zonca co-authored a new book, Generative AI on Kubernetes, which covers the operational side of running LLMs on Kubernetes. I was a reviewer on that book, and the more I read through it, the more I recognized patterns from our original work, just applied to a very different class of workload. Deployments, StatefulSets, Init Containers, DaemonSets. They were all there. Just with bigger numbers. Here is a high-level summary of how Kubernetes patterns apply to LLM workloads, which Roland and I will cover in more detail in our upcoming KubeCon + CloudNativeCon Europe 2026 talk in Amsterdam.https://generativeprogrammer.com/p/applying-kubernetes-patterns-to-llm
Cluster API is a Kubernetes subproject focused on providing declarative APIs and tooling to simplify provisioning, upgrading, and operating multiple Kubernetes clusters. Started by the Kubernetes Special Interest Group (SIG) Cluster Lifecycle, the Cluster API project uses Kubernetes-style APIs and patterns to automate cluster lifecycle management for platform operators. The supporting infrastructure, like virtual machines, networks, load balancers, and VPCs, as well as the Kubernetes cluster configuration are all defined in the same way that application developers operate deploying and managing their workloads. This enables consistent and repeatable cluster deployments across a wide variety of infrastructure environments.https://github.com/kubernetes-sigs/cluster-api
Docker-compatible container CLI built on Apple's Containerization framework. Same commands, same flags — mocker run, ps, stop, build, compose, stats — all working on macOS 26.https://github.com/us/mocker
In this guide, I'll explain how to collect Prometheus metrics from an application running in a Kubernetes cluster and push the data to a Prometheus instance on a VM in a different geographic region using Prometheus's remote write endpoint.https://medium.com/doubleverify-engineering/a-step-by-step-guide-to-securing-observability-pipelines-using-vector-by-datadog-75417454e532
Image Trust with Cosign and Kyvernohttps://cloudsecburrito.com/signed-sealed-and-admitted
In this article we deploy ArgoCD in a bare-metal Kubernetes cluster built on Proxmox.https://ruzhnikov.substack.com/p/stateless-argocd-for-bare-metal-kubernetes
How treating Service Level Objectives as declarative infrastructure changed the way I think about reliability.https://medium.com/@dpac.gdm/your-slos-should-be-kubernetes-resources-not-grafana-dashboards-8d94820e2b32
How we built an intelligent 'finally task' that turns 170,000 lines of logs into a 10-line diagnosis in under 30 seconds.https://medium.com/@happybhati/why-your-ci-cd-pipeline-failures-still-need-a-human-and-how-were-changing-that-6207a0964aac
A practical guide to building the technical-financial baseline that tells you whether autoscaling will actually save money — and how much.https://medium.com/@gabriel.arins/before-you-implement-keda-do-this-first-6bd8950b36c0
