DevOps Integration Strategies

Here’s a quick breakdown of Kubernetes deployment strategies you should know — and the trade-offs that come with each. But first — why does this matter? Because deploying isn’t just about pushing new code — it’s about how safely, efficiently, and with what level of risk you roll it out. The right strategy ensures you deliver value without breaking production or disrupting users. Let's dive in: 1. Canary ↳ Gradually route a small percentage of traffic (e.g. 20%) to the new version before a full rollout. ↳ When to use ~ Minimize risk by testing updates in production with real users. Downtime: No Trade-offs: ✅ Safer releases with early detection of issues ❌ Requires additional monitoring, automation, and traffic control ❌ Slower rollout process 2. Blue-Green ↳ Maintain two environments — switch all traffic to the new version after validation. ↳ When to use ~ When you need instant rollback options with zero downtime. Downtime: No Trade-offs: ✅ Instant rollback with traffic switch ✅ Zero downtime ❌ Higher infrastructure cost — duplicate environments ❌ More complex to manage at scale 3. A/B Testing ↳ Split traffic between two versions based on user segments or devices. ↳ When to use ~ For experimenting with features and collecting user feedback. Downtime: Not Applicable Trade-offs: ✅ Direct user insights and data-driven decisions ✅ Controlled experimentation ❌ Complex routing and user segmentation logic ❌ Potential inconsistency in user experience 4. Rolling Update ↳ Gradually replace old pods with new ones, one batch at a time. ↳ When to use ~ To update services continuously without downtime. Downtime: No Trade-offs: ✅ Zero downtime ✅ Simple and native to Kubernetes ❌ Bugs might propagate if monitoring isn’t vigilant ❌ Rollbacks can be slow if an issue emerges late 5. Recreate ↳ Shut down the old version completely before starting the new one. ↳ When to use ~ When your app doesn’t support running multiple versions concurrently. Downtime: Yes Trade-offs: ✅ Simple and clean for small apps ✅ Avoids version conflicts ❌ Service downtime ❌ Risky for production environments needing high availability 6. Shadow ↳ Mirror real user traffic to the new version without exposing it to users. ↳ When to use ~ To test how the new version performs under real workloads. Downtime: No Trade-offs: ✅ Safely validate under real conditions ✅ No impact on end users ❌ Extra resource consumption — running dual workloads ❌ Doesn’t test user interaction or experience directly ❌ Requires sophisticated monitoring Want to dive deeper? I’ll be breaking down each k8s strategy in more detail in the upcoming editions of my newsletter. Subscribe here → tech5ense.com Which strategy do you rely on most often? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

Microservice architecture has become a cornerstone of modern, cloud-native application development. Let's dive into the key components and considerations for implementing a robust microservice ecosystem: 1. Containerization:    - Essential for packaging and isolating services    - Docker dominates, but alternatives like Podman and LXC are gaining traction    2. Container Orchestration:    - Crucial for managing containerized services at scale    - Kubernetes leads the market, offering powerful features for scaling, self-healing, and rolling updates    - Alternatives include Docker Swarm, HashiCorp Nomad, and OpenShift 3. Service Communication:    - REST APIs remain popular, but gRPC is growing for high-performance, low-latency communication    - Message brokers like Kafka and RabbitMQ enable asynchronous communication and event-driven architectures 4. API Gateway:    - Acts as a single entry point for client requests    - Handles cross-cutting concerns like authentication, rate limiting, and request routing    - Popular options include Kong, Ambassador, and Netflix Zuul 5. Service Discovery and Registration:    - Critical for dynamic environments where service instances come and go    - Tools like Consul, Eureka, and etcd help services locate and communicate with each other 6. Databases:    - Polyglot persistence is common, using the right database for each service's needs    - SQL options: PostgreSQL, MySQL, Oracle    - NoSQL options: MongoDB, Cassandra, DynamoDB    7. Caching:    - Improves performance and reduces database load    - Distributed caches like Redis and Memcached are widely used 8. Security:    - Implement robust authentication and authorization (OAuth2, JWT)    - Use TLS for all service-to-service communication    - Consider service meshes like Istio or Linkerd for advanced security features 9. Monitoring and Observability:    - Critical for understanding system behavior and troubleshooting    - Use tools like Prometheus for metrics, ELK stack for logging, and Jaeger or Zipkin for distributed tracing    10. CI/CD:    - Automate builds, tests, and deployments for each service    - Tools like Jenkins, GitLab CI, and GitHub Actions enable rapid, reliable releases    - Implement blue-green or canary deployments for reduced risk 11. Infrastructure as Code:    - Use tools like Terraform or CloudFormation to define and version infrastructure    - Enables consistent, repeatable deployments across environments Challenges to Consider: - Increased operational complexity - Data consistency across services - Testing distributed systems - Monitoring and debugging across services - Managing multiple codebases and tech stacks Best Practices: - Design services around business capabilities - Embrace DevOps culture and practices - Implement robust logging and monitoring from the start - Use circuit breakers and bulkheads for fault tolerance - Automate everything possible in the deployment pipeline

I reduced our Annual AWS bill from ₹15 Lakhs to ₹4 Lakhs — in just 6 months. Back in October 2024, I joined the company with zero prior industry experience in DevOps or Cloud. The previous engineer had 7+ years under their belt. Just two weeks in, I became solely responsible for our entire AWS infrastructure. Fast forward to May 2025, and here’s what changed: ✅ ECS costs down from $617 to $217/month — 🔻64.8% ✅ RDS costs down from $240 to $43/month — 🔻82.1% ✅ EC2 costs down from $182 to $78/month — 🔻57.1% ✅ VPC costs down from $121 to $24/month — 🔻80.2% 💰 Total annual savings: ₹10+ Lakhs If you’re working in a startup (or honestly, any company) that’s using AWS without tight cost controls, there’s a high chance you’re leaving thousands of dollars on the table. I broke everything down in this article — how I ran load tests, migrated databases, re-architected the VPC, cleaned up zombie infrastructure, and built a culture of cost-awareness. 🔗 Read the full article here: https://lnkd.in/g99gnPG6 Feel free to reach out if you want to chat about AWS, DevOps, or cost optimization strategies! #AWS #DevOps #CloudComputing #CostOptimization #Startups

🕵♀️ Ever wish you could control feature rollouts without a full deployment? If yes, let's uncover the secret together. 😎 Meet the secret weapon: Feature Flag Before you dive into how you can implement it, let's talk about 'Feature Flag'. 🤔 What is Feature Flag? → Allows developers to toggle features, on and off dynamically without changing code. 💡 When do we use Feature Flag? Imagine this: You are building an e-commerce application. You've built a brand-new feature: The personalized product recommendation engine. You deploy the recommendation engine to production for everyone. ❌ Without Feature Flag, here's what could go wrong: → There's a chance it might have bugs or negatively impact user experience (e.g., slow loading times or irrelevant recommendation). → Rolling back the entire feature will require a new deployment. → It will cause downtime and potentially delaying fixes. 🤔 With the 'Feature flag' you can achieve? → Gradually release features to a subset of users. → Test out feature variations for targeted user groups. → Fix problems fast by disabling the flag. 🎩 Let's put on our Ruby on Rails hat, and start implementing Feature Flag. Here is a 3 steps process: 1. Include gem in Gemfile: "gem 'rollout'" "gem 'redis'" 2. Run "bundle install" 3. Set up Rollout in an initializer file: config/initializers/rollout.rb "$redis = Redis.new(url: ENV["REDIS_URL"], timeout: 20) $rollout = Rollout.new($redis)" 💻 It's time to see code in action. → Enable feature flag: "$rollout.activate_user(:product_recommedation_engine, @user)" → Disable feature flag: "$rollout.deactivate_user(:product_recommedation_engine, @user)" → Want to check flag is set for the @user (Will return true or false): "$rollout.active?(:product_recommedation_engine, @user)" 🎉 Voila! Now you know, the secret weapon for smoother Rails deployments! 🚀 Pro Tip: Any text enclosed in double quotes represents Ruby on Rails code that's executable in the Rails console. 🤝 Over to You: Have you used feature flags in your Rails projects? 💬 Feel free to share your thoughts or questions in the comment section. If you want to learn with me feel free to follow Chaitali Khangar Let's continue exploring the wonders of Ruby on Rails together! 🚀👨💻👩💻

𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝘄𝗶𝘁𝗵 𝗟𝗲𝗴𝗮𝗰𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗙𝗶𝗲𝗹𝗱 In a recent engagement with a large financial services company, the goal was ambitious: 𝗺𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗼𝗳 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘁𝗼 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝗮 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. 𝙏𝙝𝙚 𝙘𝙖𝙩𝙘𝙝? Much of the critical functionality resided on mainframes—reliable but inflexible systems deeply embedded in their operations. They needed to innovate without sacrificing the stability of their legacy infrastructure. Many organizations face this challenge as they 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗺𝗼𝗱𝗲𝗿𝗻 𝗰𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝘄𝗶𝘁𝗵 𝗹𝗲𝗴𝗮𝗰𝘆 systems. While cloud-native solutions promise scalability and agility, legacy systems remain indispensable for core processes. Successfully integrating these two requires overcoming issues like 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, and 𝗰𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗴𝗮𝗽𝘀. Drawing from that experience and others, here are 📌 𝟯 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 I’ve found valuable when integrating legacy functionality with cloud-based services: 𝟭 | 𝗔𝗱𝗼𝗽𝘁 𝗮 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗱𝗲𝗹 Transition gradually by adopting hybrid architectures. Retain critical legacy functions on-premises while deploying new features to the cloud, allowing both environments to work in tandem. 𝟮 | 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗔𝗣𝗜𝘀 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 Use APIs to expose legacy functionality wherever possible and microservices to orchestrate interactions. This approach modernizes your interfaces without overhauling the entire system. 𝟯 | 𝗨𝘀𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗧𝗼𝗼𝗹𝘀 Enterprise architecture tools provide a 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰 𝘃𝗶𝗲𝘄 of your IT landscape, ensuring alignment between cloud and legacy systems. This visibility 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 with Product and Leadership to prioritize initiatives and avoid redundancies. Integrating cloud-native architectures with legacy systems isn’t just a technical task—it’s a strategic journey. With the right approach, organizations can unlock innovation while preserving the strengths of their existing infrastructure. _ 👍 Like if you enjoyed this. ♻️ Repost for your network.  ➕ Follow @Kevin Donovan 🔔 _ 🚀 Join Architects' Hub!  Sign up for our newsletter. Connect with a community that gets it. Improve skills, meet peers, and elevate your career! Subscribe 👉 https://lnkd.in/dgmQqfu2 Photo by Raphaël Biscaldi  #CloudNative #LegacySystems #EnterpriseArchitecture #HybridIntegration #APIs #DigitalTransformation

What’s going on, y'all! 👋 I’m excited to announce that the documentation supporting the video I released with the Cloud Security Podcast — "How To Setup A DevSecOps Pipeline for Amazon EKS with Terraform" — has been released! 🎊 🥳 You can check out the full docs on The DevSec Blueprint (DSB) in the Projects section here: https://lnkd.in/gq-t8hSG Here’s a quick rundown of what you can learn below: ✅ Secure CI/CD Architecture: Combine AWS CodePipeline, CodeBuild, S3, SSM Parameter Store, and EKS for a seamless, end-to-end workflow. ✅ Integrated Security Scanning: Embed Snyk and Trivy checks directly into your pipeline to catch vulnerabilities before production. ✅ Infrastructure as Code: Leverage Terraform for consistent, scalable provisioning and easier infrastructure management. ✅ Containerized Deployments with EKS: Gain confidence deploying Kubernetes workloads to EKS, ensuring effortless scaling and orchestration. ✅ Proper Secrets Management: Use AWS Systems Manager Parameter Store to securely handle sensitive data, following best practices every step of the way. Check it out if you're looking to build cloud-native DevSecOps pipelines within AWS!

It took me some extra hours in late night, but here you go. I have simplified an Ideal GitHub Actions Flow for you. 👇 1) 🧭 Triggers: 🧲 GitHub Event fires → Can be a push, PR, manual dispatch, or a scheduled trigger. 📜 Workflow file executes → GitHub reads the YAML config and starts the pipeline. 🔁 Workflow Trigger hits the CI Phase → We now jump into the first main section: CI. 2) 🔧 CI Phase: 📋 Lint & Validate → Checks formatting and file syntax — like YAML, Dockerfiles, Terraform, etc. 🏗️ Build Artifacts → Your app gets compiled or packaged (Docker images, binaries, etc). 🧬 Unit Tests → Quick tests that verify individual components or logic. 🧪 Integration Tests → Validates if your services/modules interact correctly. 📊 Code Coverage → Checks how much of your code is covered by tests — helps improve test quality. 🔒 Security Scanning → Tools like CodeQL or Trivy catch vulnerabilities early. 3) 🧮 Matrix + CI Result Evaluation 🧮 Matrix Execution → Parallel jobs (across OS versions, Python/Node versions, etc). ✅ CI Results → Only proceed if everything passes — block if even one test fails. 4) 🚀 CD Phase (Continuous Deployment) 🚀 CD Phase starts → If CI is clean, we move toward releasing. 🧪 Deploy to Staging → Ship to a safe sandbox environment that mirrors production. 🔥 Smoke Tests in Staging → High-level sanity checks (e.g., “Does the login page load?”). 🛑 Approval Required → Human checkpoint — usually from senior engineer or release manager. ✅ Approval Granted → Deploy to Production → This is your official go-live moment. 🔍 Post-Deployment Tests → Sanity and health checks to ensure production is stable. 5) ♻️ Ops, Rollbacks, and Notifications 🔁 Rollback Plan (if needed) → If post-deploy tests fail, we roll back to the last good version. 📣 Notify Engineers → DevOps team gets pinged (Slack, Teams, PagerDuty, etc). 📡 Monitoring & Logging → Live dashboards, alerts, and logs keep watch over the system. 6) ✅ Final Status Updates 🟢 Update Status Badge → Those fancy CI badges on your README get updated. 📌 GitHub Repository Status reflects build/deploy result → Shows up directly on your pull request for reviewers. Get started with GitHub Actions Hands-on way: https://lnkd.in/gcReECUU Consider ♻️ reposting if you have found this useful. Cheers, Sandip Das

I used to spend days deploying an ML model.... until I discovered this. Imagine you have ✔️ defined the Machine Learning problem ✔️ trained a good model ✔️ created a REST API for your model using FastAPI, and It is time to deploy the model... but how? 🤔 Here are 3 strategies to help you, from beginner to PRO 🚀 1️⃣ 𝗠𝗮𝗻𝘂𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → Push all the Python code and the serialized model (pickle) to the GitHub repository → Ask the DevOps in the team guy to wrap it with docker and deploy it to the same infrastructure used for the other microservices, e.g. Kubernetes cluster. This approach is simple, but it has a problem. ❌ ML models need to be frequently re-trained. So you need to bother your DevOps colleague every week to refresh the model. Fortunately, there is a well-known solution for this, called Continuous Deployment (CD). 2️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 → Create a GitHub action that is automatically triggered every time you push a new version of the model to the GitHub repo → This action dockerizes and pushes the code to the inference platform (e.g. Kubernetes, AWS Lambda). This method works like a charm... ❌ until the model, you automatically pushed to production is bad. Is there a way to control model quality before deployment, and quickly decide which model (if any) should be pushed to production? 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝗲𝗱 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 The Model Registry is where you push every trained ML mode so you can → access the entire model lineage (aka what exact dataset and code generated it) → compare models → promote models to production → automatically trigger deployments via webhooks. 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲 🧠 I strongly recommend you add a Model Registry to your ML toolset, as it brings reliability and trust to the ML system and enhances collaboration between team members. ---- Hi there! It's Pau 👋 Every week I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #mlops #realworldml

Kubernetes deployment strategies are NOT one-size-fits-all. A few years ago, we rolled out a new feature using a rolling update across our microservices. It was textbook clean. Zero errors, no downtime. But guess what? ☠️ User complaints poured in within minutes. ☠️ The new logic had a bug that only appeared when v1 and v2 pods coexisted. That day I realized…  a deployment “strategy” isn’t just about uptime. It’s about context. Let’s break it down: 1. 𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 Default. Easy. But dangerous if your app state or DB migrations aren’t backward compatible. ☑️ Great for: → Stateless services → Simple patch updates   ❌ Avoid when: → There’s shared state between versions → Feature flags are not in place 2. 𝐁𝐥𝐮𝐞-𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 Zero-downtime. Fast rollback. But infra-heavy. You're duplicating environments. ☑️ Great for: → High-traffic APIs → Major version upgrades → Apps with complex dependencies ❌ Avoid when: → You can’t afford double the infra → Your team isn’t ready to manage parallel prod 3. 𝐂𝐚𝐧𝐚𝐫𝐲 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 Sexy in theory. Tricky in practice. You need metrics, observability, and automated rollback wired in. ☑️ Great for:  → Risky features → Performance testing in production → Teams with solid SRE/observability culture  ❌ Avoid when: → You’re flying blind (no dashboards, no alerts) → You don’t have progressive rollout automation (like Flagger or Argo Rollouts) Here’s what I’ve learnt. There’s no “best” deployment strategy. There’s only the one that matches your tech stack, team maturity, and business risk appetite. ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 So Others Can Learn.

The trend towards multi-cloud interoperability transforms modern IT infrastructures, allowing organizations to leverage flexibility, cost efficiency, and resilience by ensuring seamless integration across different cloud environments. Achieving effective multi-cloud interoperability relies on essential design principles prioritizing flexibility and adaptability. Cloud-agnostic coding minimizes dependencies on specific platforms, reducing lock-in risks. The microservices-based design allows applications to remain modular and scalable, making them easier to manage and integrate across diverse cloud providers. Automation, by reducing manual intervention, lowers complexity, enhances efficiency, and improves system resilience. Exposing APIs by default standardizes communication and ensures seamless interactions between components. A robust CI/CD pipeline enhances reliability and repeatability, enabling continuous updates and adaptations that meet evolving business needs. #CloudComputing #multicloud