Sandeep Girase’s Post

⚡ Day 127/180 – CROSS-RUNTIME PERF: 142ms p95 (2K USERS) Serverless, Kubernetes, ECS now identically optimized. 2K CONCURRENT USERS | LIVE BENCHMARKS: λ Serverless: **142ms** | **428 rps** | **$0.017/hr** ☸️ Kubernetes: **142ms** | **412 rps** | **$0.532/hr** 🐳 ECS: **148ms** | **398 rps** | **$0.289/hr** 🏆 KEY WINS: ✅ GraphQL batching → 42% fewer roundtrips ✅ Connection pooling → 28% faster calls ✅ CDN caching → 82% hit rate ✅ Provisioned concurrency → 0% cold starts ✅ HPA/VPA tuning → 18% better scaling yourname.com/ecom-platform ← [Serverless/K8s/ECS] ↓ Same GraphQL schema [2K users → 142ms p95 across ALL runtimes] Week 14: Production hardening complete. Truth: Production = identical perf across architectures. Your perf target for GraphQL 2026? 👇 #Performance #Serverless #Kubernetes #GraphQL #Microservices #Optimization #SRE #Java #FullStackDeveloper #CareerRoadmap #Goals #Next6Months #90Days90Blogs #BackendDeveloper #CloudNative #Kubernetes #Docker #AWS #Agile #JobsInGermany #GermanyJobs #GermanJobMarket #Stellenangebote #BerlinJobs #MunichJobs #HamburgJobs #FrankfurtJobs #CologneJobs #StuttgartJobs #JobSearch #JobSuche (German for Job Search) #NowHiring #Recruiting #OpentoWork #Career #NewJob #Opportunity #Employment #EnglishJobsGermany #RelocationGermany  #Berlin #Munich #Hamburg #Rees #Cologne #Stuttgard

Let's cut right to the chase, we recently wrapped up an 8-month project to create an internal recruitment platform! The initial problem was managing recruitment through various Excel sheets and doing manual matching. We ended up providing a full-stack role based system which included: - An admin performance dashboard - Account manager portals for managing a company and its job requirements - Recruiters creating workflows focused on matching candidates. Tech used in this project: Next.js, Node.js, PostgreSQL, AWS ECS (Docker), AWS S3, and Material UI. What we learned: - Clear requirements are important. - Prototyping is key. - DevOps will retain your sanity. - Security and data privacy are the final boss. In conclusion: Transformed a workflow that took hours into one that takes minutes. On to the next build. #FullStack #NextJS #NodeJS #PostgreSQL #AWS #SoftwareDevelopment #ProjectDelivery #Teamwork #LearningByDoing

Most beginners build a notification feature. I built a notification system. Here's the difference: ❌ Beginner: if type == "email": send_email(); if type == "sms": send_sms() ✅ What I built: A Strategy-pattern dispatcher that doesn't care what channel comes next How it works: → Async event processing via RabbitMQ — fire-and-forget from the caller's side → Thymeleaf HTML email templates with dynamic, typed payloads → Polymorphic subtypes for Payment, Welcome & Password Reset events → DLQ + retry logic so failures never vanish quietly → New notification channels plug in with zero changes to existing code Deployed on AWS — properly: → Runs in a private VPC subnet — no direct internet exposure → Only reachable through RabbitMQ, isolated by design → Managed via IAM roles, assets on S3, compute on EC2 Tech Stack: Java · Spring Boot · RabbitMQ · Docker · AWS EC2 · AWS VPC · IAM · S3 🔗 GitHub: https://lnkd.in/gm8m3J86 This is what switching from game dev to backend engineering looks like in practice — not just learning syntax, but building systems that are async, resilient, and production-aware. If you're hiring backend engineers or just want to connect, let's talk. 🚀 #Java #SpringBoot #Microservices #RabbitMQ #Docker #AWS #BackendDevelopment #SystemDesign #OpenToWork

After 10+ years in backend development, one thing I’ve learned: Building scalable systems is not just about writing code—it’s about making the right trade-offs. Recently, I worked on optimizing backend services for a large-scale email platform, improving latency by 17%. Small architectural decisions made a big difference. Key focus areas I enjoy working on: • Microservices & distributed systems • Performance optimization • Cloud-native architectures (AWS) Always open to learning and connecting with fellow engineers working on high-scale systems. #Java #SpringBoot #Microservices #AWS #SystemDesign #BackendEngineering

I recently completed a DevOps-focused technical assignment where I was given an existing backend API and tasked with deploying it to AWS using Infrastructure as Code. My focus was entirely on designing a production-style, cloud-native infrastructure that would work regardless of the backend stack (Node.js, .NET, etc.). I wanted to share what I built and what I learned 👇 🔧 What I implemented: Containerized backend API deployed on ECS Fargate Application Load Balancer as the public entry point DynamoDB for storage (on-demand) Fully defined infrastructure using AWS CDK Separate staging and production environments 🏗️ Some architecture decisions I’m proud of: Ran services in private isolated subnets (no public exposure) Avoided NAT Gateway and used VPC endpoints instead (ECR, DynamoDB, CloudWatch) → lower cost + tighter security Scoped IAM roles with least privilege (service → DynamoDB only) Environment-specific behavior (e.g., DynamoDB retention for prod) 📊 What I’d improve next: Stronger configuration management across environments CI/CD pipeline (build → push → deploy) Observability (metrics, alarms, tracing) HTTPS + WAF for production-grade security 💡 Biggest takeaway: Well-designed infrastructure should be application-agnostic — whether it’s Node.js or any other backend, the system should scale and operate consistently. Currently focusing on Node.js + cloud-native backend systems, and open to backend or DevOps opportunities. #NodeJS #AWS #DevOps #InfrastructureAsCode #ECS #CDK #Cloud #SoftwareEngineering

Most developers focus on writing code. Senior developers focus on handling failure. In real-world systems, failure is not “if” — it’s guaranteed. Here’s what changed my thinking while working on scalable Laravel systems on AWS: 🚫 Bad approach: Assume everything works fine. API calls always succeed Database is always available Queue jobs always process This is fantasy. ✅ Real-world approach: Design for failure from day one. What we implemented: 👉 Retry mechanism for failed jobs (SQS + Laravel queues) Temporary failures handled automatically 👉 Circuit breaker logic for external APIs Prevent cascading failures 👉 Graceful fallbacks If one service fails, system still responds (partial data) 👉 Proper logging & monitoring So failures are visible, not hidden Result: System didn’t crash during peak load or third-party downtime. 💡 Lesson: A system that works is normal. A system that survives failure is production-ready. Most developers don’t build for failure — that’s why systems break. What’s one failure scenario you’ve handled in your project?? #Laravel #AWS #SystemDesign #BackendDevelopment #Microservices #SoftwareEngineering #ScalableSystems #CloudComputing #DevOps #TechCareers #HiringDevelopers

Most people believe Full Stack = Frontend + Backend. That's a common shortcut — and it undersells the role significantly. The reality? A Full Stack developer navigates at least 11 distinct layers: 1 Frontend (UI/UX — React, CSS, TS) 2 Backend (APIs, Auth, Business logic) 3 Database management (SQL, NoSQL, Indexing, Migrations) 4 Server handling (Linux, Nginx, PM2, SSH) 5 Networking basics (DNS, HTTP, TCP/IP, Load balancing) 6 Cloud infrastructure (AWS, GCP, Azure, Serverless) 7 CI/CD pipelines (GitHub Actions, Docker builds, automated tests) 8 Security (HTTPS, OWASP, Secrets, WAF) 9 Containers (Docker, Kubernetes, Compose) 10 CDN & performance optimization (Caching, Compression, Edge delivery) 11 Backup & reliability (Monitoring, Snapshots, Failover) The insight that changed how I think about this: Being Full Stack isn't about knowing every layer deeply. It's about understanding how they connect — so when something breaks at layer 6, you're not lost, and when you're designing at layer 1, you're thinking about layer 9. The goal: Build, deploy, scale, and secure complete systems. If you're learning development, don't stop at just frontend/backend. Explore the ecosystem — even a surface-level understanding of each layer makes you exponentially more effective. Which layer do you think is most underrated? I'd love to hear it ↓ #FullStack #WebDevelopment #SoftwareEngineering #DevOps #Cloud #Programming #Developers #LearningJourney

🚀 AWS for Full Stack Web Development – What You Really Need to Know If you're a full stack developer, learning Amazon Web Services isn’t optional anymore — it’s a must-have skill. But the real question is: Where do you start? 🤔 💡 Here’s a simple AWS stack every full stack developer should understand: 🔹 Frontend Hosting Deploy your React/Angular apps using: S3 (static hosting) CloudFront (CDN for fast delivery) 🔹 Backend Services Build and deploy APIs using: EC2 (traditional servers) Lambda (serverless functions) 🔹 API Management API Gateway to expose and manage REST APIs 🔹 Databases RDS (SQL: PostgreSQL, MySQL) DynamoDB (NoSQL for scalability) 🔹 Storage S3 for file storage, images, and static assets 🔹 DevOps & Monitoring CI/CD with CodePipeline Logging & monitoring with CloudWatch 🔥 Why This Matters: With AWS, you can build, deploy, and scale a full stack application end-to-end — without worrying about infrastructure. 🌱 Pro Tip: Don’t just learn services — build something: Deploy a full stack app Connect frontend + backend + database Add CI/CD pipeline That’s how you stand out. 💬 Final Thought: Full stack + AWS = You own the entire application lifecycle 🚀 #AWS #FullStackDevelopment #CloudComputing #WebDevelopment #DevOps #Microservices #SoftwareEngineering

#6LayersOfaProductionGradeHealthPlatformOnAWS_EndToEnd. #ReactNextJsRESTMiddlewareC##_NETMicroservicesJenkinsDockerKubernetesAWSEKS 1 Client layer-- React + Next.js SSR/CSR hybrid for fast page loads. Patient portal, auth screens, and responsive PWA. Deployed as a Docker container behind Nginx. 2 REST middleware --API Gateway + AWS ALB JWT validation, rate limiting, OAuth2 middleware. AWS Application Load Balancer handles SSL termination and distributes traffic to K8s pods. 3 Backend-- C# / .NET microservices 4 independent services: Patient (HL7/FHIR), Auth (OAuth2 + HIPAA logs), Records (EHR + Entity Framework), Notifications (RabbitMQ). Each ships as its own container. 4 CI/CD pipeline -- Jenkins → Docker → AWS ECR Git push triggers Jenkinsfile. Multi-stage Dockerfile compiles .NET, produces a lean runtime image. Jenkins pushes to ECR and fires a Helm deployment to EKS. 5 Kubernetes -- AWS EKS NGINX Ingress, ReplicaSet ×3, HPA auto-scaling on CPU/memory, rolling zero-downtime updates, ConfigMaps + KMS-backed Secrets injected at runtime. 6. AWS managed services RDS PostgreSQL (Multi-AZ, encrypted) · S3 for patient docs · ElastiCache Redis for sessions · CloudWatch for logs, metrics, and SNS alerting. #TheDataFlow: C# .NET → Jenkins → Docker → K8s dotnet publish → Docker build → Jenkins test + tag → Push to ECR → Helm deploy → K8s rolling update → Live pods The beauty of this setup? Each .NET service is independently deployable. A change in the Notification service doesn't touch Patient or Auth. Jenkins handles the build, Docker freezes it into an image, Kubernetes rolls it out with zero downtime, and if a pod fails, K8s self-heals automatically. #HealthTech #SoftwareArchitecture #Kubernetes #DotNet #CSharp #Docker #Jenkins #AWS #ReactJS #NextJS #Microservices #DevOps #HIPAA #CloudArchitecture #FullStackDevelopment #EHR

I added a dependency to my pom.xml… and didn’t write a single line of configuration. No beans. No XML. Nothing. The application just worked. For a few minutes, I thought I had messed something up. Turns out, Spring Boot had already done the heavy lifting. It scanned the classpath, detected the dependency, and wired everything automatically through auto-configuration. That’s when it really clicked— Spring Boot isn’t just about reducing boilerplate. It’s about making smart decisions for you based on context. Under the hood, it’s all driven by things like @Conditional, @EnableAutoConfiguration, and carefully designed defaults. Once you understand that, you stop trying to control everything… and start building faster. #OpenToWork #JavaDeveloper #FullStackDeveloper #BackendDeveloper #SoftwareEngineer #SpringBoot #Microservices #Kafka #AWS #Kubernetes #Docker #RESTAPI #SystemDesign #DistributedSystems #CloudComputing #C2C #Hiring #TechJobs