Doctor Droid’s Post

Amazing. The best part is how some of these tools are fostering greater #democratization within engineering teams, by allowing for knowledge sharing, enhanced team collaboration and transparency. Makes for a tremendous improvement in organizational work culture and productivity. #engineeringteams #trends

The third and most interesting trend that I'm seeing in SRE & Platform teams: Focus on Developer Experience. What are different teams doing to improve developer experience? 1. 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀: A singular layer to access information about all technical things from services list to documentation repos. 2. 𝗦𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗰𝗮𝘁𝗮𝗹𝗼𝗴𝗶𝗻𝗴: Need a VM for some dev testing? Sure go ahead and do it yourself by filling this form. Need to spin up a new service? Sure. All the boilerplating is done. 3. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀: From Github Actions blocking users from adding new services to Production if they lack prometheus metrics to blocking PRs/MRs with poor logging practices, teams at scale are trying 4. 𝗗𝗮𝘁𝗮 𝗗𝗿𝗶𝘃𝗲𝗻 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁𝘀: Tool usage patterns, infrastructure & observability cost estimates, etc. -- Platform teams are gamifying the information for developers by making it democratically available and letting them figure out how to bring it within the org's budget instead of trying to use a carrot-stick approach. (I know a few companies that have team level notifications if their "logging budget" is going to be exhausted before time for the month -- it's quite cool, how easy it makes it for others to be informed and take quick decisions) Are you leading platform engineering? What are some of the practices that your team has taken up? I'm curious to hear more! #platformengineering #SRE #DevOps #observability

Last Call! 📣 🚂 We're gearing up for our next Observability Engineering Community London meetup, which is happening this Wednesday! This time, we've got two great talks by Dinesh Nithyanandam, Lead for Observability, Performance, and Reliability at A.P. Moller - Maersk, and Carly Richmond, Principal Developer Advocate & Manager at Elastic. 👉 Dinesh will discuss how A.P. Moller - Maersk leverages Flagger to automate canary deployments across its extensive infrastructure, which includes 120 clusters and over 1,000 microservices running in a multi-cloud environment with AKS and GKE. By integrating with monitoring tools like Prometheus, Flagger continuously evaluates key SLOs, ensuring that new versions are only fully deployed if they meet defined reliability and performance standards. 👉 Carly will draw on her experience in building applications for investment banking to explore why validating long-term feedback on feature adoption is challenging. She will discuss how combining Real User Monitoring agents, such as Elastic RUM, with OpenTelemetry tracing for backend services and Elastic Observability can help quantify user experience satisfaction and adoption, ensuring effective user experiences. 📅 Event Details: Date: Wednesday, September 11th Time: 6:00 PM Location: 24 High Holborn · London See you all there! #Observability #Engineering #SRE #SLO #devops

“Platform Engineering is the next evolution and Trace3 is here for it!” - Katherine Walther Check out this write up from Matthew Miller, who introduces many - including myself - to platform engineering. By adopting Platform Engineering, organizations can experience faster time to market, higher quality applications, lower costs, and greater innovation. Platform Engineering is a context-layered approach that requires careful planning and execution, and it will continue to evolve to incorporate innovative services such as cloud-native capabilities, microservices, serverless architecture, and AI/ML. #innovation #trace3 #DevOps #engineering https://lnkd.in/d-2JgDvR.

Great post by Tina Huang & Divanny Lamas. Thanks for sharing Gergely Orosz's article! I agree with Tina's insightful point about the shift toward a more generalized engineering skill set, supported by AI, reflecting a broader trend in the tech industry toward adaptability, resilience, and deep integration of technology and human insight. This resonates strongly: AI can indeed augment us by handling specialized, granular tasks, allowing us to focus on the bigger, holistic picture. Divany's argument for a return to simplicity is also well-supported - spot on! The article mentioned below makes some provoking thoughts. However, with the above in mind, I would respectfully disagree with the point about a shift back from microservices to monolithic architectures. Microservices were introduced to promote decoupling and simplification, not add complexity. While managing distributed components does introduce some overhead, the key downsides mentioned in the article are not inherent to the microservices architecture itself. Rather, they stem from a lack of proper "architecture" thinking and domain modeling. Organizations that adopted microservices without a deep understanding of the big picture had to pay the price. Re-introducing proper "architecture" thinking and domain modeling, as Uber did in 2020, is a natural and necessary part of software development, not a shift away from microservices. I would argue that maintaining large monolithic codebases is even harder when the original developers who understood the logic are no longer around. This often leads to band-aid solutions and spaghetti code, which makes the whole system even more vulnerable, unstable, and more "oncall hell". While managing the proliferation of services is indeed challenging, that's where tools and automation can help tremendously. As the article rightly points out, "the decade of microservices has resulted in far better tooling to build, operate and manage microservices." In the current context, microservices have actually increased the importance of Site Reliability Engineering (SRE) and AI, presenting a huge opportunity (and need) in those areas. Ultimately, I believe that automating the deployment and management of distributed (micro) services is a more surmountable challenge than maintaining overly complex monolithic logic, especially as tooling and AI capabilities continue to evolve. #monoliths #architecture #microservices #SRE #tooling

Last Call! 📣 🚂 We're gearing up for our next Observability Engineering Community London meetup, which is happening this Wednesday! This time, we've got two great talks by Dinesh Nithyanandam, Lead for Observability, Performance, and Reliability at A.P. Moller - Maersk, and Carly Richmond, Principal Developer Advocate & Manager at Elastic. 👉 Dinesh will discuss how A.P. Moller - Maersk leverages Flagger to automate canary deployments across its extensive infrastructure, which includes 120 clusters and over 1,000 microservices running in a multi-cloud environment with AKS and GKE. By integrating with monitoring tools like Prometheus, Flagger continuously evaluates key SLOs, ensuring that new versions are only fully deployed if they meet defined reliability and performance standards. 👉 Carly will draw on her experience in building applications for investment banking to explore why validating long-term feedback on feature adoption is challenging. She will discuss how combining Real User Monitoring agents, such as Elastic RUM, with OpenTelemetry tracing for backend services and Elastic Observability can help quantify user experience satisfaction and adoption, ensuring effective user experiences. 📅 Event Details: Date: Wednesday, September 11th Time: 6:00 PM Location: 24 High Holborn · London See you all there! #Observability #Engineering #SRE #SLO #devops

Ever wondered how resilient your Kubernetes setup really is? In my latest blog, I dive into Chaos Engineering and how it can help both developers and ops teams. Check out part1 of "Simplifying Chaos Engineering in Kubernetes: Step-by-Step with LitmusChaos" #kubernetes #chaosengineering #litmuschaos #devops #cloudnative

I wholeheartedly agree with the sentiments expressed in this article and appreciate the forward-thinking perspective on the evolution of the SRE role and operational practices. The point about the need for a shift towards simplicity and a more holistic approach to engineering is particularly resonant. Startups have navigated these waters of pragmatism out of necessity for years, embodying the ethos that engineering breadth—not just depth—is essential for agile and responsive development. This flexibility allows startups to prioritize projects based on business needs rather than being constrained by the specific technical skill sets of their team members. This cultural pivot towards valuing engineering breadth over hyper-specialization offers a more sustainable path forward. It enables teams to manage systems more effectively, even when individuals are stronger in some areas than others. Herein lies the potential for AI to be a transformative force. AI's capacity to augment human expertise by filling in knowledge gaps is a game-changer, particularly as we move away from the microservices paradigm that often led to siloed knowledge and expertise. The utility of AI in this context cannot be overstated. It serves not just as a tool for on-call engineers navigating the complexities of their systems but as a bridge to the vast institutional knowledge that would otherwise be inaccessible or outdated. By leveraging AI, engineers can apply their judgment more effectively, making informed decisions even in areas where they may not be experts. This ability to tap into a comprehensive, up-to-date knowledge base is crucial as we embrace more integrated and less fragmented approaches to system design and operation. In essence, the shift away from specialization towards a more generalized engineering skill set, supported by AI, reflects a broader trend in the tech industry towards adaptability, resilience, and a deep integration of technology and human insight. This evolution promises not just to address the immediate challenges of system complexity and operational sustainability but to redefine the very nature of what it means to be an engineer in the modern technological landscape. #devops #sre #engineeringleadership #ai #operations

You know that thing where you're looking for a missing piece, and then there it is? In this case, the link between top-down and bottom-up architecture in #cloudnative applications. No doubt there will be others but this is very welcome. HT Ned Bellavance and David Linthicum for the references. #SRE #terraform #CNCF #DevOps

🐳 Exploring Docker Architecture: Unveiling the Power of Containerization 🚀 In today's fast-paced tech landscape, efficiency and scalability are key to staying ahead. Enter Docker, a game-changer in modern application deployment. Let's dive into its architecture and why it's revolutionizing the way we build and ship software. 🌐 🔧 Docker Engine: The Heartbeat of Containerization At the core lies the Docker Engine, comprising the daemon (dockerd) and the CLI (docker). This duo orchestrates container management with finesse, empowering developers to package applications and dependencies into lightweight, portable containers. 📦 Container Images: Building Blocks of Agility Central to Docker's magic are container images—immutable snapshots bundling everything a container needs to run. From base OS layers to application code, images promote consistency across environments, facilitating seamless deployment across development, testing, and production. 🔗 Networking and Volumes: Bridging the Divide Docker's networking capabilities enable containers to communicate securely, while volumes ensure persistent data storage—essential for stateful applications. This flexibility fosters robust microservices architectures and scalable solutions. 🌟 Beyond Development: Transforming Deployment By abstracting infrastructure complexities, Docker streamlines DevOps workflows. Continuous Integration/Continuous Deployment (CI/CD) pipelines thrive on Docker's ability to accelerate testing, deployment, and rollback processes—paving the way for rapid innovation. 🌍 Global Impact: Empowering Digital Transformation From startups to enterprises, Docker fuels innovation worldwide. Its portability and compatibility with cloud-native technologies like Kubernetes amplify its role in modernizing legacy systems and building cloud-native applications. 🔍 Looking Ahead: Docker and the Future As Docker evolves, its ecosystem grows richer with tools like Docker Compose, Docker Swarm, and Docker Hub, augmenting its capabilities in container orchestration, multi-container applications, and secure image management. ℹ️ Get Started Today: Embrace Docker Whether you're a developer, IT professional, or business leader, Docker offers a transformative edge. Explore its architecture, experiment with containerization, and unlock new possibilities for your projects and organization. 🚀 Join the Container Revolution Ready to elevate your development and deployment strategies? Embrace Docker and harness the power of containerization. Let's build the future of applications, one container at a time. 🌟 #Docker #Containerization #DevOps #DigitalTransformation #CloudNative #TechInnovation #LinkedInLearning