☁️ Mohamed Bouchenafa

🚀DevOps, SRE & Platform Engineer's, if you want to stand out do this...🚀 ☁️When writing your resume or LinkedIn profile make it super easy for hiring managers to see the BEST things you have done🎖️🏆 ☁️Explain what you have done CLEARLY & SPECIFICALLY and not simply put generic statements about an application or tool you have used🤓🛠️ Every hiring manager I have worked with has explained that it is highly desirable for them to be able to see the best work engineers have done on their profiles quickly and easily💡 #devops #SRE #platform #resume #tethr

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🚀 Thinking of a Career in DevOps? Read This First! 🚀 Full Video : https://lnkd.in/eSGy7vYH DevOps is no longer just about mastering tools—it's about mastering code. The game has changed from tool-based operations to a deep integration of code with processes. If you're aiming for success in DevOps, coding knowledge, and an understanding of SDLC and TDLC are must-haves. 📈 In the past, non-IT professionals could transition into DevOps by learning the tools, but today, the landscape demands more. From 'Pipeline as Code' to 'Security as Code,' the focus has shifted from PPT—People, Process, Tools—to PPC—People, Process, Code. 💻 Thinking DevOps might be easy to break into? Think again. While learning is possible, those with coding experience have a significant edge in this competitive market. 🌟 #DevOps #CareerInTech #CodingLife #TechTransformation #ITCareer #PipelineAsCode #InfrastructureAsCode #SDLC #TechCareers #DevOpsEngineer #CareerAdvice #TechEvolution

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Managed Kubernetes Services vs On-Prem. The gist 👇 ✅ Control Planes are abstracted away from you with managed k8s. ✅ You have less ability to customize with managed k8s, but less work needs to be done. ✅ Bare-metal allows you to use whatever plugins you want and fully embark on however you want your environment to look. In this episode of the Engineering Revised podcast, Kristina and I break down the pros and cons of each so you can decide what works best in your environment. https://lnkd.in/eVYEjSsf #kubernetes #devops #platformengineering

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🔒 How do you manage secrets in your Kubernetes environment? 🤔 Kubernetes is challenging when it comes to secret management, that’s a fact. How do you ensure your secrets are secure? Do you use a centralized or decentralized approach? Where do you keep your secrets? One effective tool is Sealed Secrets by Bitnami. This tool offers a secure way to encrypt secrets before they reach your cluster, ensuring that sensitive data is protected at all times and can be safely stored in a Git repository. #devops #kubernetes

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What is DevOps really about? Is it about delivering IT faster and adding more value? I think Simona Zarskute's blog captures it quite well. DevOps affects your entire business operation. https://lnkd.in/eB3z7qVD #devops #dasadevops #leadership #brunelagilecommunity

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A Kubernetes Service is a way to set an endpoint for your Pods. By default, Pods are ephemeral. That means you don't want to route people or other parts of the application to them. Instead, you want to have a set location to route users and other apps to that doesn't change. That's a Kubernetes Service. It also helps with load balancing. For example, you can have 3-4 Pods sitting behind a Service. #kubernetes #devops #platformengineering

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Google Kubernetes Engine (GKE) is a managed Kubernetes service provided by Google Cloud. It allows you to run, manage, and scale containerized applications using Kubernetes. GKE automates many of the tasks involved in managing Kubernetes clusters, making it easier to deploy, manage, and scale containerized applications. ### Key Features of GKE: 1. **Managed Kubernetes:** GKE manages the Kubernetes control plane, so you don't have to worry about tasks like cluster provisioning, upgrades, and monitoring. 2. **Auto-Scaling:** GKE can automatically scale your clusters based on the workload. It supports both horizontal pod autoscaling (scaling the number of pods) and cluster autoscaling (scaling the number of nodes). 3. **Automatic Upgrades:** GKE provides automatic updates and patches to the Kubernetes control plane, ensuring your cluster remains secure and up-to-date. 4. **Security:** GKE offers built-in security features, including IAM integration, network policies, and private clusters. It also integrates with Google's security services, such as Google Cloud Armor and Cloud Identity. 5. **Hybrid and Multi-Cloud Support:** GKE can be used in hybrid or multi-cloud environments, allowing you to run your workloads on-premises or across multiple cloud providers. 6. **Monitoring and Logging:** GKE integrates with Google Cloud's monitoring and logging services, providing visibility into your clusters and workloads. 7. **Customizability:** While GKE is a managed service, it allows for a high degree of customization, letting you tailor the cluster to meet your specific needs. 8. **Service Mesh Integration:** GKE integrates with Istio and other service meshes to provide advanced networking, security, and observability features for microservices architectures. ### Use Cases: - **Microservices:** GKE is ideal for deploying and managing microservices, providing the tools needed for scaling, service discovery, and fault tolerance. - **CI/CD Pipelines:** GKE can be integrated into continuous integration and continuous delivery (CI/CD) pipelines to automate the deployment of applications. - **Data Processing:** GKE is suitable for running big data and machine learning workloads due to its ability to scale on demand. Overall, GKE simplifies the management of Kubernetes clusters, allowing you to focus on building and running your applications rather than managing the underlying infrastructure.

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Platform Engineering vs Traditional Infrastructure Management 🛠️ In today's tech landscape, understanding the shift from traditional infrastructure management to platform engineering is crucial. Both play vital roles, but they differ significantly in approach and outcomes. Traditional infrastructure management focuses on maintaining and managing physical and virtual servers, networks, and storage. It's about ensuring that the hardware and software are up and running, often involving manual processes and reactive problem-solving. This approach has been the backbone of IT for decades, providing stability and reliability. Platform engineering, on the other hand, is a more modern approach. It emphasises automation, scalability, and self-service capabilities. Engineers build platforms that developers can use to deploy and manage applications effortlessly. This method reduces the need for manual intervention and allows for faster, more efficient workflows. The goal is to create a seamless environment where development and operations can collaborate more effectively. One of the key differences lies in the tools and technologies used. Traditional infrastructure management relies heavily on tools like VMware and traditional monitoring systems. Platform engineering leverages containerisation, orchestration tools like Kubernetes, and Infrastructure as Code (IaC) practices. This shift enables more agile and scalable solutions, aligning with the needs of modern businesses. What are your thoughts on this evolution? Comment below or connect with me if you're looking to hire or explore new opportunities. Visit charles-simon.co.uk for more insights. #PlatformEngineering #Infrastructure #TechInnovation

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Interesting report from New Relic showing some massive growth rate for Java 17, now becoming the most popular LTS version of Java. The report from New Relic shows that only 9% of applications were using Java 17 during production in 2023 but now around 35% of apps are using it, with an increase of nearly 300%. In terms of newer JDKs , 1.4% of applications are using Java 21 after 6 months of release, which may not seem like a lot, but Java 17 was only at 0.37% in the same timeframe. The report is only based on several 100,000s of applications so doesn't necessarily provide a global picture, however it may lean towards the idea that early adoption of new JDKs is increasing dramatically. Link below for the PDF, any thoughts from my network? https://lnkd.in/eS_Wjb7k #java #jdk #java17 #java21 #oracle #newrelic #software #softwaredev #jobs #career #hays

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Terraform's Import block functionality will help give a lot of companies the ability to bring their existing infrastructure under a unified code base. Now with the ability to use for_each inside their import block this video shows a cool way to utilize that feature to build on top of your existing pipelines without having to re-engineer anything.

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🚀 AWS CodePipeline now supports stage-level conditions, enhancing your release processes with quality and compliance checks before and after stages. Customize conditions via console, API, CLI, CloudFormation, or SDK to ensure robust and flexible deployments. 💻🔧🛡️ https://lnkd.in/gezgJ7Cw #AWS #DevOps #CloudComputing

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One of my beefs with terraform is it’s trying so hard to be flat. Some of us want very dynamic scripts. But when you create a k8s cluster, form the kubexonfig file, then need to use the kubexonfig file to load in the rest of the parts like ingress, having a depends_on would be nice for providers. I did cheat a little and abuse the variable system to force the provider to wait, but then you run into what about a workspace that will have kubernetes set to false and get a zero count for your k8s module? Then it gets angry because the module output isn’t there. I still like Chef. Chef let me do what I want when I want. Just was bad on creating the underlying infrastructure but great at configuring it once it was up. *shrug* #terraform #kubernetes #k8s #eks #automation #burgerkingmodel

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#GCP #Cloud #Architecture #learning What are these services? Compute Engine - Virtual machines. You reserve a configuration of CPU, memory, disk, and GPUs, and decide what OS and additional software to run. Kubernetes Engine - Managed Kubernetes clusters. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. You create a cluster and configure which containers to run; Kubernetes keeps them running and manages scaling, updates and connectivity. Cloud Run - A fully managed serverless platform that runs individual containers. You give code or a container to Cloud Run, and it hosts and auto scales as needed to respond to web and other events. App Engine - A fully managed serverless platform for complete web applications. App Engine handles the networking, application scaling, and database scaling. You write a web application in one of the supported languages, deploy to App Engine, and it handles scaling, updating versions, and so on. Cloud Functions - Event-driven serverless functions. You write individual function code and Cloud Functions calls your function when events happen (for example, HTTP, Pub/Sub, and Cloud Storage changes, among others). What level of abstraction do you need? If you need more control over the underlying infrastructure (for example, the operating system, disk images, CPU, RAM, and disk) then it makes sense to use Compute Engine. This is a typical path for legacy application migrations and existing systems that require a specific OS. Containers provide a way to virtualize an OS so that multiple workloads can run on a single OS instance. They are fast and lightweight, and they provide portability. If your applications are containerized then you have two main options. What is your use case? Use Compute Engine if you are migrating a legacy application with specific licensing, OS, kernel, or networking requirements. Examples: Windows-based applications, genomics processing, SAP HANA. Use GKE if your application needs a specific OS or network protocols beyond HTTP/s. When you use GKE, you are using Kubernetes, which makes it easy to deploy and expand into hybrid and multi-cloud environments. Anthos is a platform specifically designed for hybrid and multi-cloud deployments. It provides single-pane-of-glass visibility across all clusters from infrastructure through to application performance and topology. Example: Microservices-based applications. Use Cloud Run if you just need to deploy a containerized application in a programming language of your choice with HTTP/s and websocket support. Examples: websites, APIs, data processing apps, webhooks. Use App Engine if you want to deploy and host a web based application (HTTP/s) in a serverless platform. Examples: web applications, mobile app backends

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[💡Large Scale Architecture Sharing] ELB Sharding on AWS Some of my customers rapidly scale up their architecture, requiring their Application Load Balancer (ALB) to support high Queries Per Second (QPS). I found a blog about ALB sharding, which is quite useful for supporting high QPS. ELB automatically scales up and down, and scales in and out in response to traffic load, to help manage requests by optimally routing incoming traffic. ALB can scale up to a maximum of 100 active nodes created across all AZs. Once the load balancer reaches close to the maximum supported 100 active nodes across all AZs, sharding becomes necessary. This post discussed: 🟢 When you need to shard ALB (provide command to check) 🟢 How to use Route53 with weighted routing policy to shard the ALB. 🟢 Explained limitations, such as how Sticky Session might be affected. Good to read. 👉Post: https://lnkd.in/dnfttJVQ #aws #elb #sharding ————————— Follow me for more architecture design sharing: Alick Wong

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PaaS Offer (available below) requires end user to deploy an Azure container instance. The image includes: * Latest Ubuntu Release * PostgresQL 16 * 1 core and 1.5gb RAM * 4 open ports https://lnkd.in/gB_hcUr8

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You can create your own Kubernetes extension (an Operator) to interact with whatever API you'd like (as long as you have access or it's public). When you create an Operator, you'd use something like Kubebuilder. Once you figure out a framework (like Kubebuilder) to use, you can use Go to programmatically configure how you want the Operator to look. Here's an example. #kubernetes #devops #platformengineering

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Why is creating Kubernetes clusters still difficult? There shouldn't be a massive amount of configuration needed. k8s clusters should have: 1. The resources they need to run your app stack. 2. Ingress routes where necessary. 3. Proper secret management. 4. Scaling capabilities and that's it... So why the complexity? In this blog post, I break down a CLI-based approach for deploying Kubernetes because all you need is a few prompts to deploy a cluster. #kubernetes #devops #platformengineering https://buff.ly/3ZqviWk

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Awesome to see that is borning a new way of creating and deploying Kubernetes Operators, but now written in C#. #dotnet #csharp #microsoft #kubernetes #k8s #k8soperator

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