Praveen Singampalli’s Post

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 The DevOps/SRE engineer always are responsible to handle the production issues and to save your from any escalation this post is curated: 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 : A single-machine Kubernetes learning environment has a single point of failure. Creating a highly available cluster means considering: - Separating the control plane from the worker nodes. - Replicating the control plane components on multiple nodes. - Load balancing traffic to the cluster’s API server. - Having enough worker nodes available, or able to quickly become available, as changing workloads warrant it. 𝐒𝐜𝐚𝐥𝐞 : If you expect your production Kubernetes environment to receive a stable amount of demand, you might be able to set up for the capacity you need and be done. However, if you expect demand to grow over time or change dramatically based on things like season or special events, you need to plan how to scale to relieve increased pressure from more requests to the control plane and worker nodes or scale down to reduce unused resources. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐜𝐜𝐞𝐬𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 : You have full admin privileges on your own Kubernetes learning cluster. But shared clusters with important workloads, and more than one or two users, require a more refined approach to who and what can access cluster resources. You can use role-based access control (RBAC) and other security mechanisms to make sure that users and workloads can get access to the resources they need, while keeping workloads, and the cluster itself, secure. You can set limits on the resources that users and workloads can access by managing policies and container resources.

DevOps is trickier than we think. There are many technical and cultural aspects you need to get right in order to enjoy the benefits of this approach. First and foremost, build a solid platform 🛠️. A platform is a set of technologies and processes that allows your product teams to deliver, deploy, and operate software applications with ease. Automate everything 🤖. Eliminate or minimize manual intervention on infrastructure and applications. Prefer declarative manifests over imperative commands or scripts. Be frugal (KISS) 🤓. Choose your tech stack carefully. As Gregor Hohpe highlights in his "Platform Strategy" book, you need to be aware of the difference between a Sinking Platform and a Floating Platform. Use data to drive innovation. Collect feedback from infrastructure, applications, and, most importantly, your teams. Address any concern. Increased scale, new technologies, and new requirements will make your solutions obsolete unless you keep adapting and improving them. https://lnkd.in/d8_ga7fs

After years of wrestling with containers, kubernetes clusters, and Devops/SRE chaos, here's what I learned: 1. Performance issues in production rarely match your local environment. "It works on my machine" is now "It works in my namespace." 2. Your monitoring stack will grow more complex than your application. Prometheus metrics are the new logging statements. 3. Kubernetes manifests get messy faster than your Git history. YAML is both your best friend and worst enemy. 4. Auto-scaling is never as "auto" as you think. The art is in the HPA configuration, not the pod spec. 5. Helm charts start simple, then explode in complexity. Values.yaml becomes your new documentation. 6. Service mesh promises vs. reality are worlds apart. Istio is powerful, but complexity comes at a cost. 7. CI/CD pipelines are living organisms. They grow, evolve, and occasionally bite back. 8. Database operations in containers require a special kind of patience. StatefulSets are where DevOps engineers earn their battle scars. 9. Cloud costs optimize themselves... said no one ever. FinOps is the new DevOps. 10. Security scanners generate more alerts than insights. The art is knowing which CVEs actually matter. 11. Disaster recovery plans never survive first contact with reality. Chaos engineering isn't a luxury, it's a necessity. 12. The best architecture is the one your team can debug at 3 AM. Simplicity beats cleverness every time. Truth bomb: If you're not regularly questioning your infrastructure choices, you're not pushing hard enough. What are your thoughts?

While learning devops tools I came across a containerization platform called Docker so sharing the basic concepts on Docker I hope it will be meaningful. What is Docker? Docker is a platform for developing, shipping, and running applications in containers. Containers are a standard unit of software that packages up code and its dependencies so the application runs quickly and reliably on any machine. 2) Containers vs. Virtual Machines Containers are much lighter than virtual machines (VMs). VMs require a full operating system to run, while containers share the host operating system's kernel. This makes containers much faster to start and stop, and they require fewer resources. 3) What problems does Docker solve? Docker solves a number of problems for developers and operations teams, including: * Portability: Docker containers can be run on any machine that has Docker installed, regardless of the operating system. This makes it easy to move applications between development, testing, and production environments. * Scalability: Docker containers can be easily scaled up or down to meet demand. This is because they are lightweight and can be started and stopped quickly. * Reliability: Docker containers are more reliable than traditional deployment methods because they are isolated from each other. This means that if one container fails, it is less likely to affect other containers. Docker Architecture The Docker architecture consists of three main components: * Docker Engine: The Docker Engine is the core of the Docker platform. It is responsible for creating and managing containers. * Docker Hub: Docker Hub is a cloud-based registry for storing and sharing Docker images. * Docker CLI: The Docker CLI is a command-line interface for interacting with the Docker Engine. Docker Files and Images A Dockerfile is a text document that contains a set of instructions for building a Docker image. An image is a read-only template that contains the software and configuration needed to run a container. Building a Docker Image To build a Docker image, you need to create a Dockerfile and then run the docker build command. This command will read the instructions in the Dockerfile and create an image. Running a Container To run a container, you need to use the docker run command. This command will create a new container from an image and start it. Conclusion Docker is a powerful tool that can be used to simplify the development, deployment, and management of applications. If you are looking for a way to improve your development and operations processes, Docker is a great option to consider. #Docker #DevOps #Containerization

Learn terraform from basics to advanced in the devops course by Praveen Singampalli starting from tomorrow https://lnkd.in/gsryE-Bj Terraform is an infrastructure as code tool that lets you define both cloud and on-prem resources in human-readable configuration files that you can version, reuse, and share. It creates and manages resources on cloud platforms and other services through their application programming interfaces (APIs). Providers enable Terraform to work with virtually any platform or service with an accessible API. The core Terraform workflow consists of three stages: Write: You define resources, which may be across multiple cloud providers and services. For example, you might create a configuration to deploy an application on virtual machines in a Virtual Private Cloud (VPC) network with security groups and a load balancer. Plan: Terraform creates an execution plan describing the infrastructure it will create, update, or destroy based on the existing infrastructure and your configuration. Apply: On approval, Terraform performs the proposed operations in the correct order, respecting any resource dependencies. For example, if you update the properties of a VPC and change the number of virtual machines in that VPC, Terraform will recreate the VPC before scaling the virtual machines. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐮𝐫𝐬𝐞 by Praveen Singampalli ?? Easy to learn with 14 projects The best in DevOps tools included for 2024 Mock interviews/resume reviews/doubts session Live Classes/recorded sessions Each session backed by a project Roles and responsibilities document for each project 𝐄𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐜𝐨𝐮𝐫𝐬𝐞 :  https://lnkd.in/gsryE-Bj [ Last 5 seats available ] Note - The Live classes DevOps batch8.0 is starting from tomorrow, see you in the class :)

Roadmap to learn devOps 🖥️🗺️🌐 1. learn the basics: - understand what devops is and its principles 📚 - study agile and lean methodologies 📘 2. learn a programming language: - start with python, bash, or powershell 🐍 3. understand version control systems: - learn git basics and use github or gitlab 🌐 4. study continuous integration/continuous deployment (ci/cd): - learn tools like jenkins, travis ci, or circleci 🔄 5. get familiar with configuration management: - study tools like ansible, puppet, or chef 🛠️ 6. learn about containerization: - understand docker and create basic containers 📦 7. explore container orchestration: - study kubernetes and its components ⚙️ 8. understand cloud platforms: - learn aws, azure, or google cloud ☁️ 9. study infrastructure as code (iac): - learn terraform or cloudformation 🏗️ 10. get familiar with monitoring and logging: - use tools like prometheus, grafana, or elk stack 📊 11. learn about networking and security: - understand basic networking concepts and security practices 🌐 12. study automated testing: - implement unit tests, integration tests, and end-to-end tests 🧪 13. explore service mesh: - learn about istio or linkerd for managing microservices 🚀 14. get hands-on experience: - work on projects and contribute to open-source projects 🧑💻 15. join devops communities: - participate in forums, attend meetups, and join online communities 💬 16. practice with real-world scenarios: - set up ci/cd pipelines, deploy applications, and manage infrastructure 🔧 17. understand devops metrics and kpis: - learn how to measure and improve devops performance 📈 18. explore serverless architectures: - study aws lambda, azure functions, or google cloud functions 📦 19. learn about automation tools: - study tools like jenkins, bamboo, or gitlab ci/cd 🛠️ 20. stay updated: - follow industry blogs, read books, and take advanced courses 📚 this roadmap will guide you through the essential steps to become proficient in devops #devops #DevOps #cybersec #learning

The adoption of LLMs in DevOps is on the rise, with innovative tools showcasing their potential. This blog post delves into the current applications of LLMs in enhancing DevOps processes and looks ahead to their promising future applications. #DevOps #LLMs #Innovation https://lnkd.in/e6c-Wkhc

Excited to publish my blogs on one of best DevOps blogging platform DevOps.com. Many DevOps professionals are wondering if generative AI will be a friend or foe, even as DevOps remains at the forefront when it comes to onboarding new technologies. Our DevOps communities have been enabling and fostering the collaborative cultural shift and evolving roles and responsibilities of software professionals. In this article, we explore the impact of generative AI in key DevOps roles and explain how to prepare ourselves for the shift https://lnkd.in/g2wr6v-k

🚀 Is GitOps the Future of DevOps? 🚀 As we look toward 2025 and beyond, the landscape of DevOps is evolving rapidly. GitOps is gaining momentum as a game-changer in managing infrastructure and applications. This article dives deep into the trends and predictions shaping the future of DevOps. 🌐 At TechNet Resources Inc., we’re always on the pulse of the latest  DevOps and AI/ML solutions innovations to help our clients stay ahead in this dynamic space. Check it out 👇 and let us know your thoughts! https://lnkd.in/e3fh5SDH #DevOps #GitOps #Innovation #TechTrends #TechNetDifference

𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐝𝐞𝐩𝐥𝐨𝐲 𝐚𝐧 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐧 𝐭𝐡𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐮𝐬𝐢𝐧𝐠 𝐀𝐫𝐠𝐨𝐂𝐃 In the world of DevOps, there is a constant battle between confusion and hierarchy. ✍️ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes. It follows the GitOps pattern of using Git repositories as the source of truth for defining the desired application state. With ArgoCD, you can automate the deployment of applications as defined in a Git repository to your target cluster environments. But an ArgoCD is more than just a tool. It is a force for good in the world of DevOps. It is a tool that can help you to tame the chaos, bring order to the chaos, and achieve your DevOps goals. 𝐓𝐡𝐢𝐬 𝐩𝐨𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬: ✅Automate your deployments: ArgoCD can automatically deploy your applications to your cluster, either in response to a Git commit, a trigger from a CI/CD pipeline, or a manual user request. This can save you a lot of time and effort. ✅Observability : Developers can quickly find if the application state is in sync with the desired state. ArgoCD comes with a GUI and CLI which helps to quickly inspect the application and find differences between the desired and the current live state. ✅Operation : ArgoCD UI visualizes the entire application resource hierarchy, not just top-level resources defined in the Git repo. For example, developers can see ReplicaSets and Pods produced by the Deployment defined in Git. From the UI, you can quickly see Pod logs and the corresponding Kubernetes events. This turns ArgoCD into a very powerful multi-cluster dashboard. So if you are looking for a tool that can help you to tame the chaos in your DevOps world, then ArgoCD is the tool for you. With ArgoCD; ✅ You can bring order to the chaos ✅ Achieve your DevOps goals and ✅ Save your team time and effort. The battle for order in the world of DevOps is real. But with ArgoCD, you can be the victor. Credits: Chandresh Desai #devops #aws #azure