#Terraform and #OpenTofu dominate the infrastructure-as-code (#IaC) world. But how do you choose between them? 🤔 This post will help you understand how they work, their features, similarities, and differences — so you can make the choice that works for you 🙌 https://hubs.li/Q02KWc9l0 #devops #platformengineering
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nxs-universal-chart: lots of knowledge - one Helm chart How do people come to the idea of developing their chart? We will not only share our experience, but also remember what is Helm, get acquainted with the advantages and features of such universal chart, and show how it all works. Take a look at our article to find this out: https://lnkd.in/dNqPWeAW If you haven't tried out nxs-universal-chart - head over to GitHub: https://lnkd.in/guPcH7DX You also can ask any questions, get help, or provide feedback in our community channel: https://lnkd.in/dz43s22Y #DevOps #OpenSource #Nixys #SRE
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Where every day is an opportunity to deploy something amazing. #DevOpsLife #ContinuousImprovement #AutomateEverything #DevOpsJourney #InfrastructureAsCode #CICDPipeline #DevOpsTransformation #CloudNative #AgileDevOps #DevSecOps #MicroservicesArchitecture #ContainerizationNation #KubernetesCluster
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Platform Engineering: The Secret Weapon for Cloud-Native Success 🚀 This week, we'd like to share a game-changing trend in the tech world: Platform Engineering! 🔗https://lnkd.in/dp4BupAR Are your developers drowning in Kubernetes complexity? It's time to throw them a lifeline. A recent study reveals some mind-blowing stats: 🌟 96% of orgs already have platform engineering teams 🌟 56% adopt it for scalability and flexibility 🌟 Execs are 1.8x more likely to see it as a promotion Is your organization leveraging platform engineering to its full potential? Share your experiences with us in the comments! 💭 #PlatformEngineering #CloudNative #DevOps #Kubernetes
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🚀 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠: 𝐍𝐨𝐝𝐞𝐏𝐨𝐫𝐭, 𝐏𝐨𝐫𝐭, 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐏𝐨𝐫𝐭, 𝐚𝐧𝐝 𝐓𝐚𝐫𝐠𝐞𝐭𝐏𝐨𝐫𝐭 🚀 Understanding simple terms will help you with a large number of resource connectivity. 𝐍𝐨𝐝𝐞𝐏𝐨𝐫𝐭: Exposes a service on a specific port of each node, enabling external traffic to access the service. 🌐 𝐏𝐨𝐫𝐭: The port where a service listens for incoming traffic inside the cluster. 📡 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐏𝐨𝐫𝐭: The port that a container inside a pod listens to for traffic. 🛠️ 𝐓𝐚𝐫𝐠𝐞𝐭𝐏𝐨𝐫𝐭The port on the container that the incoming traffic is forwarded to from the service. 🎯 🔖 #Kubernetes #DevOps #Networking #CloudNative #K8sBestPractices #ITInfrastructure #Containerization #help #contact #query #doubts Feel free to contact me if you have any queries or doubts. Happy to help you.
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DevSecOps | System Design | CNCF Falco | MLOps | Microservices 🌐 APIs | Ex- Mercedes Benz USA, Ericsson, Apollo 247 | Azure 2x AWS | K8s ☸️ Docker 🐳
🚀 𝐔𝐬𝐢𝐧𝐠 Kubeshark 𝐭𝐨 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐈𝐬𝐬𝐮𝐞𝐬 𝐢𝐧 𝐌𝐲 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 🚀 In the world of distributed systems, application performance can make or break the user experience. Recently, I encountered some latency issues in our application that were impacting the overall responsiveness. 🔄 To dig deeper, I turned to Kubeshark – an incredible tool that provides a real-time API traffic analyzer for Kubernetes clusters. 🛠️ Here's how Kubeshark helped me solve the issue: 1. Captured real-time traffic 🧐 across the entire Kubernetes cluster. 2. Analyzed packet flows, HTTP requests, and application logic to pinpoint bottlenecks. 3. Identified delays in specific API calls that were causing the latency. 4. Optimized the application architecture and reduced the overall response time. Used optimsed and synchronised timeouts with delays. After some PnCs I got the right configuration for the ingress. Kubeshark not only simplified the troubleshooting process but also saved hours of manual effort by offering a transparent look into the microservices traffic. 🚀✨ #Kubernetes #Kubeshark #DevOps #MLOps #PerformanceOptimization #LatencyFix #Troubleshooting #Microservices #CloudNative
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Centralized vs. distributed—two approaches to monitoring as code with unique strengths. 🌐 Explore how these methods impact consistency, scalability, and resilience in your infrastructure. Coralogix dives deep into the pros and cons of each. 🚀 Which one is best for your system? #MonitoringAsCode #InfrastructureManagement #DevOps #Coralogix
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Centralized vs. distributed—two approaches to monitoring as code with unique strengths. 🌐 Explore how these methods impact consistency, scalability, and resilience in your infrastructure. Coralogix dives deep into the pros and cons of each. 🚀 Which one is best for your system? #MonitoringAsCode #InfrastructureManagement #DevOps #Coralogix
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Cloud Security Analyst @ Snowbit by Coralogix, Detection & Response, CSPM & CWPP, Vulnerability Management, Ex AWS Community Builder, Volunteer at HIH Community
Centralized vs. distributed—two approaches to monitoring as code with unique strengths. 🌐 Explore how these methods impact consistency, scalability, and resilience in your infrastructure. Coralogix dives deep into the pros and cons of each. 🚀 Which one is best for your system? #MonitoringAsCode #InfrastructureManagement #DevOps #Coralogix
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Centralized vs. distributed—two approaches to monitoring as code with unique strengths. 🌐 Explore how these methods impact consistency, scalability, and resilience in your infrastructure. Coralogix dives deep into the pros and cons of each. 🚀 Which one is best for your system? #MonitoringAsCode #InfrastructureManagement #DevOps #Coralogix
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Thank you Arpit for the nice post about Kubeshark. #Kubernetes #DevOps #DevSecOps
DevSecOps | System Design | CNCF Falco | MLOps | Microservices 🌐 APIs | Ex- Mercedes Benz USA, Ericsson, Apollo 247 | Azure 2x AWS | K8s ☸️ Docker 🐳
🚀 𝐔𝐬𝐢𝐧𝐠 Kubeshark 𝐭𝐨 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐈𝐬𝐬𝐮𝐞𝐬 𝐢𝐧 𝐌𝐲 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 🚀 In the world of distributed systems, application performance can make or break the user experience. Recently, I encountered some latency issues in our application that were impacting the overall responsiveness. 🔄 To dig deeper, I turned to Kubeshark – an incredible tool that provides a real-time API traffic analyzer for Kubernetes clusters. 🛠️ Here's how Kubeshark helped me solve the issue: 1. Captured real-time traffic 🧐 across the entire Kubernetes cluster. 2. Analyzed packet flows, HTTP requests, and application logic to pinpoint bottlenecks. 3. Identified delays in specific API calls that were causing the latency. 4. Optimized the application architecture and reduced the overall response time. Used optimsed and synchronised timeouts with delays. After some PnCs I got the right configuration for the ingress. Kubeshark not only simplified the troubleshooting process but also saved hours of manual effort by offering a transparent look into the microservices traffic. 🚀✨ #Kubernetes #Kubeshark #DevOps #MLOps #PerformanceOptimization #LatencyFix #Troubleshooting #Microservices #CloudNative
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