Simone Ostini

Book Review: Mastering the Red Hat Certified Engineer (RHCE) Exam Luca Berton is a known personality in the community for his work on Ansible. He has written many best-selling books, such as Ansible for VMware by Examples, Ansible for Kubernetes by Examples, Red Hat Ansible Automation Platform, and Hands-on Ansible Automation. His experience in automation and Red Hat technologies is well reflected in this book. The book introduces the reader to the value of RHCE, exam methods, and curriculum, thus encouraging individuals to work more sincerely in the IT world. Although I always refer them to the official Red Hat training for their Red Hat certification exams, reading this book will definitely boost their confidence for the RHCE exam. Luca also gives a clear outline of different types of certifications with Red Hat and career tracks, which is useful for anyone considering a Red Hat learning path. It covers some of the essential basics of Red Hat Enterprise Linux (RHEL), taking concepts from the core and then gradually building toward system administration, which was enjoyable. Chapters regarding logging and storage are nicely structured and written. The actual RHCE material starts with automation. Luca does a great job introducing Ansible, covering all the basic concepts along with practical guides. The book proceeds with writing Ansible playbooks and small automation tasks for Linux as well as Windows environments. The sections on Ansible continue with managing networks, storage, files, services, security, and more, all for automation-related tasks. One of the best chapters of the book is the troubleshooting and performance optimization. These are important topics for any RHCE candidate, and Luca explains them in a way that is easy to understand and apply. A key component of RHCE is its hands-on, performance-based testing. This book includes lab exercises that should not be skipped. Completing the labs based on the guides provided many times will significantly enhance the chances of success in the exam. Big thanks to Luca Berton for creating such an excellent resource for the learning community. Thanks to Tushar Khattar and BPB Publications for this book copy. Highly recommended for anyone preparing for RHCE! Book Review: https://lnkd.in/gXpvcKWS Get your copy: https://a.co/d/a7SElGr #RHCE #RedHat #Certification #ExamPreparation #Linux #RHEL #Ansible #Automation #SystemAdministration #Troubleshooting #PerformanceOptimization #Networking #Storage #Security #Playbooks #PracticalExercises #HandsOnLearning #ITCertification #OpenSource #BPBPublications #LucaBerton #RedHatAnsible #CareerGrowth

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Scaling resources in your Kubernetes clusters is key to keeping costs in check. Using Kubernetes Event-Driven Autoscaler (KEDA) can help. The example below shows how pods scale based on SQS queue depth, including scaling to zero when idle. This approach is useful for reducing costs on workloads with variable traffic patterns. The example included watching pods scale up to handle 30,000 messages, then back down. It also shows monitoring with k9s and detailed cleanup steps to avoid leftover resources. The article below from Dallin is helpful for teams implementing event-driven scaling in their Kubernetes environments. https://lnkd.in/egvrfAgy

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🔥 Stop Scrolling! Every DevOps Engineer Needs This Monitoring Cheat Sheet ⚙️ If you’ve ever wondered how Prometheus and Grafana actually work together to give you a real-time pulse of your infrastructure — this guide nails it 👇 💡 Why It’s a Gem: 🔍 Explains monitoring, Prometheus & Grafana in plain English ⚙️ Step-by-step setup on EC2 (Prometheus, Grafana & Node Exporter) 🧠 Understand how Prometheus “pulls” metrics & how Grafana visualizes them 📊 Build beautiful, real-time dashboards 🚨 Quick intro to Alertmanager for proactive notifications ✅ Comes with ready-to-run commands — no fluff, just action This single PDF can save you hours of trial and error, helping you truly understand monitoring instead of just copying random commands. 📘 Attaching: Prometheus-Grafana_Monitoring_CheatSheet.pdf — a must-save for anyone in DevOps or Cloud Engineering. 💬 If this helps you, smash ❤️, drop a “MONITORING” in the comments, and share it with your team — let’s make monitoring simple, visual, and powerful! 🚀 #DevOps #Monitoring #Grafana #Prometheus #CloudComputing #Observability #SRE #Automation #Infrastructure #TechCommunity

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A review of how to expose apps in #AKS, because I need to clarify my thoughs sometimes. From the simple kubernetes service to the Gateway API, using #cilium I have a lot to do on the gateway api before being in the clear 😅 https://lnkd.in/eUMfb5zH

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🚀 Rolling Deployments with Octopus Deploy: 4 Key Steps + Pros & Cons Rolling deployments are a powerful way to deliver updates incrementally while keeping systems reliable and users happy. Using Octopus Deploy, here’s the step-by-step process I follow: 4 Steps for Rolling Deployments: 1️⃣ Preparation: Validate the new application version in a staging environment to ensure it’s production-ready. 2️⃣ Incremental Update: Update a small subset of instances at a time, removing them from the load balancer to prevent serving traffic during updates. 3️⃣ Health Checks: Monitor metrics and run automated tests to confirm the updated instances are functioning as expected. 4️⃣ Update Load Balancer: Once instances pass health checks, add them back to the load balancer and repeat the process for the next subset. Pros of Rolling Deployments: ✅ Minimal Downtime: Services remain available as updates roll out incrementally. ✅ Early Issue Detection: Problems are caught early in the rollout, limiting their impact. ✅ Easy Rollbacks: If an issue arises, rolling back to the previous version is quick. Cons to Consider: ⚠️ Longer Deployment Times: Sequential updates can take longer compared to other strategies. ⚠️ Version Compatibility Risks: Mixed versions during rollout can lead to unexpected issues. With Octopus Deploy, these steps become seamless, from automating health checks to managing load balancers. In one instance, using this approach reduced downtime by 40% while ensuring a smooth user experience. What’s your experience with rolling deployments? Let’s share strategies! #OctopusDeploy #RollingDeployments #DevOps #SRE #BestPractices #InfoDataWorx

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Understanding Kubernetes Resources and Controllers As Kubernetes (K8s) continues to dominate the container orchestration space, understanding its core components is key to mastering efficient and scalable deployments. Today, let us break down two essential concepts that make K8s tick: 1) Resources 2) Controllers. Kubernetes Resources: Resources in Kubernetes represent the desired state of various components that run in the cluster. These include: ==>Pods: The smallest unit of deployment, representing a set of containers running together. ==>Deployments: Ensure the right number of replicas of your pods are running and manage updates smoothly. ==>Services: Enable stable network access to applications, even when pods are dynamically created or destroyed. ==>ConfigMaps & Secrets: Store and manage application configurations and sensitive data. ==>Persistent Volumes: Manage storage that outlives individual pod lifecycles. Kubernetes Controllers: Controllers are responsible for ensuring that the actual state of the system matches the desired state. If something goes wrong (e.g., a pod crashes or a deployment doesn’t have the right number of replicas), controllers take corrective actions to bring things back into the desired state. Here are some of the most common ones: ==>Deployment Controller: Ensures the correct number of pod replicas are running, even as updates or changes are made. ==>ReplicaSet Controller: Maintains a specified number of pod replicas, replacing any pods that are terminated or fail. ==>StatefulSet Controller: Manages the deployment of stateful applications, providing each pod with a stable identity and persistent storage. ==>Horizontal Pod Autoscaler (HPA): Automatically adjusts the number of pod replicas based on metrics like CPU or memory usage. ==>DaemonSet Controller: Ensures a pod runs on every node in the cluster for tasks like monitoring, logging, or networking. How Resources and Controllers Work Together In Kubernetes, the interaction between resources and controllers is dynamic and ongoing.  Controllers constantly monitor resources to ensure they match the desired state defined in your configuration files.  If there is a discrepancy (e.g., a pod crashes), the controller will take action — like restarting a pod or scaling a deployment — to bring everything back into line. #Kubernetes #CloudNative #DevOps #Containers #Microservices #K8s #KubernetesResources #KubernetesControllers

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🚀 Automating Deployments with GitLab CI/CD + Runners In modern DevOps, CI/CD pipelines are key to faster, more reliable software delivery. With GitLab CI/CD, you can automate everything — from code commit → build → test → deploy — all within a single platform. 💡 The real power comes from GitLab Runners, which execute your jobs in the pipeline. They can be: ⚙️ Shared Runners – Managed by GitLab for convenience. 🧱 Specific Runners – Dedicated to your project or team. 🐳 Custom Runners – Run on Docker, Kubernetes, or even bare-metal servers. Here’s a quick example 👇 stages: - build - test - deploy build_app: stage: build script: - echo "Building app..." - docker build -t myapp:latest . tags: - docker-runner run_tests: stage: test script: - pytest tests/ deploy_prod: stage: deploy only: - main script: - kubectl apply -f k8s/deployment.yaml tags: - kube-runner ✅ Highlights: Fully integrated with GitLab repositories Easy to scale using multiple Runners Supports Docker, Kubernetes, and cloud environments Built-in testing, security, and deployment automation 🚀 GitLab CI/CD + Runners = End-to-end DevOps automation! #DevOps #GitLab #CICD #Automation #Kubernetes #Docker #Terraform #GitLabRunner #ContinuousIntegration #ContinuousDeployment #CloudEngineering ---

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