Cloud Application Deployment

Demystifying CI/CD Pipelines: A Simple Guide for Easy Understanding 1. Code Changes:   Developers make changes to the codebase to introduce new features, bug fixes, or improvements. 2. Code Repository:   The modified code is pushed to a version control system (e.g., Git). This triggers the CI/CD pipeline to start. 3. Build:   The CI server pulls the latest code from the repository and initiates the build process.   Compilation, dependency resolution, and other build tasks are performed to create executable artifacts. 4. Predeployment Testing:   Automated tests (unit tests, integration tests, etc.) are executed to ensure that the changes haven't introduced errors.   This phase also includes static code analysis to check for coding standards and potential issues. 5. Staging Environment:   If the pre deployment tests pass, the artifacts are deployed to a staging environment that closely resembles the production environment. 6. Staging Tests:   Additional tests, specific to the staging environment, are conducted to validate the behavior of the application in an environment that mirrors production. 7. Approval/Gate:   In some cases, a manual approval step or a set of gates may be included, requiring human intervention or meeting specific criteria before proceeding to the next stage. 8. Deployment to Production:   If all tests pass and any necessary approvals are obtained, the artifacts are deployed to the production environment. 9. Post deployment Testing    After deployment to production, additional tests may be performed to ensure the application's stability and performance in the live environment. 10. Monitoring:    Continuous monitoring tools are employed to track the application's performance, detect potential issues, and gather insights into user behaviour. 11. Rollback (If Necessary):    If issues are detected post deployment, the CI/CD pipeline may support an automatic or manual rollback to a previous version. 12. Notification:    The CI/CD pipeline notifies relevant stakeholders about the success or failure of the deployment, providing transparency and accountability. This iterative and automated process ensures that changes to the codebase can be quickly and reliably delivered to production, promoting a more efficient and consistent software delivery lifecycle. It also helps in catching potential issues early in the development process, reducing the risk associated with deploying changes to production.

12 Microservices Best Practices : 1. Orchestration with Kubernetes Why it matters: - Automated container management - Efficient resource utilization - Self-healing capabilities - Rolling updates with zero downtime Best Tool: Kubernetes has become the de facto standard, with alternatives like Docker Swarm for simpler deployments. 2. API Gateway Implementation Critical aspects: - Request routing - Authentication & authorization - Rate limiting - Response caching - API versioning Popular choices: Kong, AWS API Gateway, or Netflix Zuul 3. Containerization with Docker Key benefits: - Consistent environments - Isolated dependencies - Rapid deployment - Version control for entire application stacks Pro tip: Use multi-stage builds to optimize container sizes 4. Security Best Practices Must-haves: - OAuth2/JWT implementation - Service-to-service authentication - Secrets management - Regular security audits - Network policies Remember: Security is a continuous process, not a one-time setup 5. CI/CD Pipeline Excellence Essential components: - Automated testing - Infrastructure as Code - Continuous monitoring - Automated rollbacks - Feature flags Tools to consider: Jenkins, GitLab CI, GitHub Actions 6. Single Responsibility Principle Guidelines: - One service = one business capability - Clear domain boundaries - Independent deployability - Autonomous teams Warning: Avoid the "nanoservice" trap! 7. Database Per Service Key considerations: - Data autonomy - Appropriate database selection - Clear ownership - Schema independence Challenge: Managing distributed transactions 8. Observability Triad Focus areas: - Metrics (what's happening) - Logging (why it happened) - Tracing (where it happened) Tools: ELK Stack, Prometheus, Jaeger 9. Event-Driven Architecture Benefits: - Loose coupling - Better scalability - Improved resilience - Asynchronous processing Technologies: Kafka, RabbitMQ, AWS SNS/SQS 10. Stateless Design Principles: - No session affinity - Cloud-native ready - Horizontal scalability - Resilience to failures 11. Scalability Patterns Strategies: - Horizontal scaling - Load balancing - Caching - Database sharding - Command Query Responsibility Segregation (CQRS) 12. Resilience & Fault Tolerance Implementation: - Circuit breakers - Retry policies - Fallback mechanisms - Bulkhead pattern Tool spotlight: Resilience4j, Hystrix Pro Tips for Success: 1. Start Small: - Begin with a monolith - Gradually break into services - Learn from each iteration 2. Monitor Everything: - Service health - Business metrics - Infrastructure metrics - User experience 3. Documentation is Key: - API specifications - Dependencies - Deployment procedures - Troubleshooting guides 4. Team Structure: - Conway's Law - Two-pizza teams - Clear ownership - Cross-functional capabilities Common Pitfalls to Avoid: - Premature decomposition - Shared databases - Synchronous communication dependencies - Inadequate monitoring - Insufficient automation

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!

Let's 𝗱𝗲𝗽𝗹𝗼𝘆 a REST API to 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 🐳 Step by step ↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 🤔 Two weeks ago we built a REST API to serve historical data on taxi rides in NYC. And last week we wrote a professional Dockerfile to package it inside a Docker image. The API works like a charm on our laptop, but the thing is, until you don’t deploy it to a production environment, and make it accessible to > your clients 💁🏻♀️ > your colleagues 👨🏻💼 > or even the whole world 🌏 your real-world impact is ZERO. So today, I want to show you how to deploy this API to a Kubernetes cluster. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀? ☸📦 Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of your Dockerized apps. Kubernetes is a powerful beast. However, it has also one BIG problem.. 𝗧𝗵𝗲 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗬𝗔𝗠𝗟 𝗛𝗘𝗟𝗟 🔥 The Kubernetes configuration file required to deploy even the simplest service is > very verbose, > error-prone and > excessively complex. which adds too much friction (and frustration!) in your deployment process. So the question is >> 𝗜𝘀 𝗶𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝘁𝗼 𝗱𝗲𝗽𝗹𝗼𝘆 𝘁𝗼 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗼𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗬𝗔𝗠𝗟 𝗵𝗲𝗹𝗹? Yes, it is! ⬇️ 𝗚𝗶𝗺𝗹𝗲𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗿𝗲𝘀𝗰𝘂𝗲 🦸🏻 Gimlet  ↳🔗 https://gimlet.io/ is a tool running inside your Kubernetes cluster that helps you quickly deploy your apps. Let's start with a manual deployment: 𝗠𝗮𝗻𝘂𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 🔧 These are the steps to create a new deployment with the Gimlet UI > 𝗜𝗺𝗽𝗼𝗿𝘁 your github repository. > Choose to manually deploy from a 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲 > Pick your 𝗗𝗼𝗰𝗸𝗲𝗿 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝘆, and > Set the 𝗽𝗼𝗿𝘁 𝗻𝘂𝗺𝗯𝗲𝗿 your API is listening to. BOOM! Your API is now running in Kubernetes. Let's go one step further... 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗖𝗜/𝗖𝗗 ⚙️ The Gimlet Github action helps you automatically deploy your code changes to your Kubernetes cluster. For example, you can add a github workflow that > 𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝘀 after every push to main branch > 𝗧𝗲𝘀𝘁𝘀 your code > 𝗕𝘂𝗶𝗹𝗱𝘀 and pushes the Docker image to your Docker registry (in this case, I use mine from Github), and > 𝗗𝗲𝗽𝗹𝗼𝘆𝘀 it to Kubernetes using the Gimlet action. Continuous delivery made simple! 𝗙𝘂𝗹𝗹 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 👨💻 Below you will find a link to the Github repository will all the code ⬇️ ---- Hi there! It's Pau Labarta Bajo 👋 Every day 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 #docker #kubernetes #mlops #realworldml

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation

"ARM CPUs + Apache Kafka = A Perfect Match for Edge AND Cloud" Real-time #datastreaming is no longer limited to powerful servers in central data centers. With the rise of energy-efficient #ARM CPUs, organizations are deploying #ApacheKafka in #edgecomputing, in addition to the widespread hybrid #cloud environments—unlocking new levels of scalability, flexibility, and sustainability. In my blog post, I explore how ARM-based infrastructure—like #AWSGraviton or industrial IoT gateways—pairs with #eventdrivenarchitecture to power use cases across #manufacturing, #retail, #telco, #smartcities, and more. ARM CPUs bring clear benefits to the world of #streamprocessing: - High energy efficiency and low cost - Compact form factors ideal for disconnected edge environments - Strong performance for modern #IoT and #AI workloads The combination of Kafka and ARM enables more cost-efficient and sustainable applications such as: - Predictive maintenance on the factory floor - Offline vehicle telemetry in #transportation and #logistics - Local compliance automation in #healthcare - In-store analytics and loyalty systems in food and retail chains Read the full post with use cases, architecture diagrams, and tips for building cost-effective, resilient, real-time systems at the edge and in the cloud: https://lnkd.in/eeJ6mcaH

I’ve optimized infra for 50+ companies. Here’s the 𝐎𝐍𝐄 𝐦𝐢𝐬𝐭𝐚𝐤𝐞 they all made. ☠️ Everyone thinks cloud scaling is about adding more servers, more clusters, and more resources. It’s not! The #1 mistake killing your cloud efficiency? ☠️ Scaling infra before fixing inefficiencies. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐬𝐞𝐞 90% 𝐨𝐟 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐝𝐨𝐢𝐧𝐠:  🚩 Cloud costs start creeping up. 🚩 Performance issues appear. 🚩 The team’s knee-jerk reaction? “Scale up.” Throwing more compute at an inefficient system is like upgrading from a sedan to a sports car… with the handbrake still on. 🤡 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐬𝐜𝐚𝐥𝐞 𝐛𝐞𝐟𝐨𝐫𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠: → 𝐘𝐨𝐮 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐲 𝐢𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐢𝐞𝐬. ↳ That one bloated query? ☠️ 𝐍𝐨𝐰 𝐢𝐭’𝐬 𝐛𝐮𝐫𝐧𝐢𝐧𝐠 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 10𝐱 𝐦𝐨𝐫𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬. Your infra bill looks like a startup’s growth chart, but it’s just wasted spend. Performance issues don’t go away. Because you never solved the actual bottlenecks. 🤡 I once worked with a company that was spending $1M+/month on cloud. They thought they needed more servers. Instead, we optimized their workload scheduling, fixed bad autoscaling policies, and killed zombie workloads. Outcome? Cloud costs dropped by 40%. Without adding a single new instance. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐜𝐚𝐥𝐞, 𝐚𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟:  ✅ Are my workloads right-sized? ✅ Is my autoscaling logic actually working? ✅ Where am I over-provisioning? ✅ Are my queries, caches, and jobs optimized? 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐭𝐡𝐞 𝐥𝐚𝐬𝐭 𝐬𝐭𝐞𝐩, 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭. 𝐌𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐝𝐨𝐧’𝐭 𝐧𝐞𝐞𝐝 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐫𝐚. 𝐓𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐛𝐞𝐭𝐭𝐞𝐫 𝐢𝐧𝐟𝐫𝐚. How are you optimizing before scaling? DevOps | Cloud Cost Optimization | AWS

Most Frequently Asked System Design Interview Question: Load Balancer Sirf Code Nahi, Traffic bhi Handle Karna Aana Chahiye Imagine this : You’re at a crowded highway toll booth, and every lane (server) is getting cars (requests). One lane has 3 cars, another has 20. The queue’s uneven, slow, and frustrating. That's exactly what happens when there's no Load Balancer in your architecture. => Now the interview twist: “Can you design a scalable system like YouTube or Zomato where traffic doesn’t crash your servers ?” Let’s break it down like you’d pitch it during your interview: How Does a Load Balancer Work? Step 1: Traffic Inflow - When a client sends a request (Req1, Req2...), the load balancer catches it before it hits the backend. Step 2: Smart Dispatch - It looks at the current load across servers and decides which one to forward the request to. But here's the interviewer-loved part — the logic behind it: ➣ Load Balancing Algorithms You Should Mention in Interviews ➣ Round Robin: Like taking turns — each server gets one request in a cycle. ➣ Weighted Round Robin: Give more load to stronger servers (e.g., 50% traffic to high RAM machine). ➣ Sticky Sessions (Sticky Round Robin): Same user hits the same server — important for sessions/login. ➣ IP/URL Hashing: Uses hash value of IP or URL to consistently route to the same server. ➣ Least Connections: Chooses the server with the fewest active connections. ➣ Least Response Time: Chooses the fastest responding server. Helps in low-latency systems. => Types of Load Balancers (Mention this when asked about deployment) 🔹 Software-based: NGINX, HAProxy – easier to configure, install on VMs. 🔹 Hardware-based: Dedicated appliances – used in legacy or high-performance environments. Why This Gets You Bonus Points in Interviews ✅ Shows you understand real-world traffic management ✅ Highlights system resilience — you’re not designing systems that crash under load ✅ Gives you edge when asked “How will your system scale?” Interview Tip: If the interviewer asks, “What happens when one server crashes?” → You bring in the Failover concept. The load balancer detects the failure and reroutes to healthy servers automatically. Smooth experience, zero downtime. Want to sound even sharper? Drop this line: “I’d also integrate health checks with my load balancer to ensure traffic only hits active, responsive servers.” Boom. That's the line that gets the nod. Wrap-up Thought: Load balancer isn’t just a tool. It’s your system’s first line of defense during high traffic days. (Think IPL live stream, Diwali sales, result day on govt portals) 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Happy learning !

Open RAN deployment scenarios: Cloud platform In cloud platform, different logical functions (O-DU, O-CU, near real time RIC) of open RAN can be deployed in many ways. These functions can be kept at edge cloud (within 20km of cell site) or at regional cloud (within 200km of cell site) depending upon: a)  services to be provided by telcos - eMBB, mMTC, URLLC b)  availability of fronthaul, midhaul (stringent requirement for O-DU, O-CU at edge) c)  type of deployment - indoor, outdoor, number of O-RU per O-CU There are six scenarios to deploy different functions: 1.     Scenario A - Near-RT RIC, O-CU, O-DU functions are virtualized at edge. Deployments with an abundance of fronthaul capacity to meet O-DU latency. 2.     Scenario B - Near-RT RIC is virtualized on a regional cloud, and O-CU, O-DU are virtualized on an edge. Deployments in locations with limited fronthaul capacity and limited number of O-RUs while still meeting the O-DU latency requirements. 3.     Scenario C - Near-RT RIC and O-CU are virtualized on a regional cloud, and O-DU on an edge. Scenario C.1 - O-CU control plane in the regional cloud whereas user plane can be shifted to edge basis service requirement for network slices. One vO-DU present. Scenario C.2 (not mentioned in the picture)– as compared to C1, there are different vO-DU instances. One driver for C.2 is RAN (O-RU) sharing among operators. Second, operator can customize scheduler functions. 4.     Scenario D - Similar to scenario C, but O-DU is supported by physical NF 5.    Scenario E - In contrast to scenario D, O-DU and O-RU are virtualized at cell site 6.     Scenario F – Similar to scenario D but O-DU and O-RU are virtualized Note - User Plane (UP) traffic is delivered to the UPF (edge/regional). To learn more, visit - https://lnkd.in/eukU26Ne