AWS in Plain English ☁️ If you're getting started with Amazon Web Services, here’s a simple way to understand the core services: • EC2 → Your virtual computer in the cloud • S3 → Scalable storage for files and data • RDS → Managed relational (SQL) databases • Lambda → Run code without managing servers • API Gateway → Entry point for creating and managing APIs • CloudWatch → Monitoring, logs, and alerts • VPC → Your isolated private network in the cloud • IAM → Controls access and permissions • CloudFront → Global content delivery for faster performance • DynamoDB → Fully managed NoSQL database • SNS → Push notifications and messaging • SQS → Reliable message queuing service 💡 Master these, and about 80% of AWS becomes much easier to understand. #AWS #CloudComputing #TechSimplified #DevOps #Learning #Cloud #SoftwareEngineering

Automated EC2 Backups: Saving Time and (AWS) Money ☁️💰 I’m excited to share a project I’ve been working on to streamline cloud infrastructure management! Manually managing EBS snapshots is not only time-consuming but can lead to unexpected costs if old data isn't pruned. To solve this, I built a serverless EC2 Backup & Retention Pipeline using AWS Lambda and Amazon EventBridge. How it works: ✅ Tag-Driven: The Lambda function automatically identifies instances with a Backup: true tag. ✅ Automated Snapshots: It captures EBS snapshots across all attached volumes. ✅ Cost-Efficient: An automated 7-day retention policy ensures old snapshots are deleted, keeping storage costs optimized. ✅ Resilient: Integrated with Amazon SNS for real-time error notifications. This project reinforces my interest in combining software engineering with business process efficiency—ensuring that our cloud environments are not just powerful, but also cost-effective and secure. Check out the full documentation on my GitHub! https://lnkd.in/eSQKi7Vb #AWS #CloudComputing #DevOps #Python #Serverless #Automation #SoftwareEngineering #Medtech #HECMontreal

6 months ago, our AWS bill was ₹7,00,000/month. Last month? ₹3,50,000. Same infrastructure. Same traffic. ~400 RPS. 25+ microservices. Here are the 5 exact steps that cut our cloud costs by 50% ───────────────────────── 𝟭. 𝗘𝗖𝟮 𝗥𝗶𝗴𝗵𝘁-𝗦𝗶𝘇𝗶𝗻𝗴 — The biggest win We were running 18 EC2 instances. Most were oversized "just in case." What I did: → Pulled 2 weeks of CPU/memory utilization from CloudWatch → Identified instances running at <20% average load → Downsized or consolidated them Result: 18 instances → 7 instances Savings: ~₹2L+/month from this step alone ───────────────────────── 𝟮. 𝗖𝗹𝗼𝘂𝗱𝗪𝗮𝘁𝗰𝗵 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — $400 → $10/month Our CloudWatch bill was $400/month. Nobody had questioned it. What I found: → Logs retained for 365 days (we needed 30) → High-frequency metrics firing every 1 min (changed to 5 min) → Duplicate alarms across overlapping dashboards Fixed the retention policies + metric intervals. Bill dropped to $10/month. In one week. ───────────────────────── 𝟯. 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗣𝗼𝗱 𝗥𝗶𝗴𝗵𝘁-𝗦𝗶𝘇𝗶𝗻𝗴 Pods had resource requests/limits set at deployment and never revisited. What I did: → Used Prometheus + Grafana to track actual pod usage over 2 weeks → Identified pods with 4x over-provisioned memory → Updated resource manifests across 25 microservices Bonus: API latency dropped to ~220ms after right-sizing. Fewer nodes needed = lower node costs. ───────────────────────── 𝟰. 𝗠𝗼𝘃𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗢𝘂𝘁 𝗼𝗳 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 We had databases running inside EKS with persistent volumes. Costing us in storage, IOPS, and operational complexity. Migrated to AWS RDS: → No more manual backups eating compute → Multi-AZ handled by AWS → Freed up ~3 nodes reserved for DB workloads ───────────────────────── 𝟱. 𝗦𝟯 + 𝗖𝗹𝗼𝘂𝗱𝗙𝗿𝗼𝗻𝘁 𝗳𝗼𝗿 𝗦𝘁𝗮𝘁𝗶𝗰 𝗔𝘀𝘀𝗲𝘁𝘀 Django was serving static/media files directly from EKS pods. Every image request was hitting compute. Migrated to S3 + CloudFront: → Reduced pod CPU load significantly → Faster delivery via CDN → S3 costs a fraction of EC2 bandwidth ───────────────────────── 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗹𝗲𝘀𝘀𝗼𝗻? Cloud waste doesn't announce itself. It hides in default settings, forgotten retention policies, and configs nobody questions. A 2-week audit changed everything. ───────────────────────── If you're in DevOps or SRE — when did you last audit your cloud costs? Drop a comment I'd love to hear what you found. #DevOps #SRE #AWS #CloudCost #Kubernetes #Terraform #CloudOptimization #FinOps #EKS #PlatformEngineering

EC2 vs Lambda vs ECS vs Fargate. Every AWS architect has been asked: "Which compute should we use?" Here's the honest breakdown: 🖥️ EC2 → You need full control over the OS → Long-running workloads with predictable traffic → Legacy apps that can't be containerized Cost: Highest. You pay whether it's idle or not. ⚡ Lambda → Event-driven, short bursts of execution → You want zero infrastructure management → Unpredictable or sporadic traffic Cost: Lowest entry point. But cold starts will humble you at scale. 🐳 ECS / Fargate → Containerized workloads without managing clusters → Microservices that need more than 15 minutes to run → Teams already living in Docker Cost: Middle ground. Pay per task, not per server. The real lesson? There's no universally "best" compute on AWS. There's only the right tool for your workload, your team, and your budget. Choosing EC2 for a simple API is over-engineering. Choosing Lambda for a 30-minute batch job is a mistake waiting to happen. Know your workload first. Pick the service second. #AWS #CloudComputing #DevOps #SoftwareArchitecture #EC2 #Lambda #Serverless #Site_reliability_engineer

AWS has 200+ services. You'll actually use about 10. Here are the ones that matter — explained without the marketing language: **EC2** — A virtual machine in the cloud. You get a server you can SSH into and run anything on. **S3** — File storage. Put files in, get them back later. Infinitely scalable. Cheap. **Lambda** — Run code without managing a server. Upload a function, it runs when triggered. Pay per execution. **RDS** — Managed relational database. PostgreSQL or MySQL without the ops headache. **CloudFront** — CDN that makes your static content load fast globally. **IAM** — Access control. Who can do what. Critical for security. Get this wrong and everything else is at risk. **API Gateway** — Expose your Lambda functions or services as HTTP APIs. **SQS** — Message queue. Services send messages, other services process them. Great for decoupling. **ECS/EKS** — Run containers. ECS is easier. EKS is Kubernetes (more complex, more control). **Route 53** — DNS. Connect your domain to your AWS infrastructure. Start with S3 + Lambda + IAM. Most applications are built from these three. Save this — share with anyone starting their cloud journey. #AWS #CloudComputing #Developer #DevOps #TechFinSpecial

𝗦𝘁𝗮𝘁𝗲𝗹𝗲𝘀𝘀 𝘃𝘀 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹... 𝗼𝗻𝗲 𝘀𝗰𝗮𝗹𝗲𝘀, 𝗼𝗻𝗲 𝗯𝗿𝗲𝗮𝗸𝘀 (𝗶𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗻𝗼𝘁 𝗰𝗮𝗿𝗲𝗳𝘂𝗹) Once you start deploying on the cloud, state becomes something you think about constantly. ➡️ 𝗦𝘁𝗮𝘁𝗲𝗹𝗲𝘀𝘀 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 holds nothing locally, no memory, no disk dependency. Swap out an instance, spin up a new one, nothing breaks. This is what makes horizontal scaling and load balancing actually work. ➡️ 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 require persistence. Databases, queues, file storage. The data has to outlive the process handling it, which is exactly why managed services like RDS and S3 exist. They're designed specifically to handle persistence reliably. The pattern is straightforward. 𝗞𝗲𝗲𝗽 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗹𝗮𝘆𝗲𝗿 𝘀𝘁𝗮𝘁𝗲𝗹𝗲𝘀𝘀 𝗮𝗻𝗱 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲 𝘀𝘁𝗮𝘁𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘁𝗼 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗶𝘁. 𝘌𝘊2 handles requests, 𝘙𝘋𝘚 owns the data, 𝘚3 owns the files. This separation is also what makes your infrastructure resilient. A stateless EC2 instance behind an ALB can be terminated and replaced automatically. ⚠️ A poorly architected stateful instance going down takes your data with it. 𝙊𝙣𝙘𝙚 𝙩𝙝𝙞𝙨 𝙘𝙡𝙞𝙘𝙠𝙨, a lot of AWS architecture decisions start making sense. Why WordPress runs on EC2 but the database lives on RDS, why media gets offloaded to S3, why sessions get stored in ElastiCache rather than on the instance itself. #aws #devops #terraform #cloud #linux

☁️ AWS finally made simple (no more confusion) Most people quit cloud because it feels too complex. But the truth is… you only need to understand a few core services to unlock everything. Here’s AWS in plain English: • EC2 → your computer in the cloud • S3 → a giant unlimited folder • RDS → a managed SQL database • Lambda → code that runs automatically • API Gateway → doorway to your APIs • CloudWatch → logs + monitoring • VPC → your private network • IAM → security guard of AWS • CloudFront → global CDN for speed • DynamoDB → NoSQL database • SNS → sends notifications • SQS → message queue that never sleeps Master these → and suddenly AWS starts making sense. Don’t try to learn everything. Learn the right things first. Which AWS service confused you the most when you started? 👇 #AWS #CloudComputing #WebDevelopment #DevOps #Backend #SoftwareEngineering #TechSimplified #LearnToCode #Developers #Programming

🌐 What is a VPC in AWS? | Types + Real-World Example When you use AWS, your resources (like EC2, databases, etc.) don’t just float around randomly. They live inside something called a VPC (Virtual Private Cloud) — your own private network in the cloud. ✅ What is a VPC? A VPC is a custom-defined virtual network in AWS. Think of it like your personal data center, where you control: • Which resources go where • Who can access them • How data flows between them You manage everything: ✔ IP address range ✔ Subnets (public & private) ✔ Route tables ✔ Security groups (firewalls) 🌍 Real-World Example: Online Store Imagine you're building an e-commerce app: 🟢 Web Server → Public Subnet (accessible to users) 🔒 Database → Private Subnet (secured, no direct internet access) 🌐 Internet Gateway → Connects public resources to the internet 🔁 NAT Gateway → Allows private servers to access the internet safely 👉 This setup ensures security + scalability + control 🧱 Types of VPC in AWS 1️⃣ Default VPC • Automatically created in every region • Pre-configured with public subnets & internet access • Best for beginners 2️⃣ Custom VPC • Built manually as per your architecture • Full control over subnets, routing, gateways • Ideal for production-level systems 🔐 Why use a VPC? ✔ Secure and isolated environment ✔ Full control over networking ✔ Better architecture design ✔ Easily scalable 🎯 Takeaway VPC is the foundation of every AWS project. If you understand VPC well, you’re already ahead in cloud computing 🚀 💬 Have you tried creating a custom VPC yet? Let’s discuss in the comments! #AWS #VPC #CloudComputing #DevOps #Networking #FreshersInCloud #LearningAWS #CloudEngineer #WomenInTech

AWS Guide : 1. What is AWS and why is it used? Amazon Web Services (AWS) is a comprehensive cloud computing platform that provides on-demand services such as computing, storage,  networking, databases, analytics, and more. * Why AWS is used: Scalability – Easily scale resources up or down based on demand High Availability – Multi-region and fault-tolerant architecture Cost Optimization – Pay-as-you-go pricing model Faster Deployment – Rapid provisioning of infrastructure 2. What is EC2? Amazon EC2 (Elastic Compute Cloud) provides resizable virtual servers in the cloud. *Key Features: Launch Linux/Windows instances Auto Scaling support Security Groups (firewall control) Elastic IP for static addressing * Use Case: Deploy and run Spring Boot microservices. 3. What is S3? Amazon S3 (Simple Storage Service) is a highly scalable object storage service used for storing and retrieving data. * Key Features: 99.999999999% durability Versioning support Lifecycle management Fine-grained access control * Use Case: Store logs, images, and file uploads. 4. What is RDS Amazon RDS (Relational Database Service) is a managed service for relational databases. * Supported Databases: MySQL PostgreSQL Oracle SQL Server * Key Features: Automated backups Multi-AZ deployment Read replicas for scaling * Use Case: Backend database for Spring Boot applications. 5. What is CloudWatch? Amazon CloudWatch is used for monitoring AWS resources and applications in real time. * Key Features: Centralized logging Metrics and dashboards Alerts and alarms * Use Case: Monitor EC2 performance and application logs. 6. What is Lambda? AWS Lambda allows you to run code without provisioning or managing servers. * Use Cases: Trigger-based processing (e.g., S3 uploads) Background jobs and automation 7. What is API Gateway? Amazon API Gateway enables you to create, publish, and manage RESTful APIs. * Use Case: Expose backend microservices to frontend applications. 8. How do you secure AWS applications? Use AWS IAM roles and policies Configure Security Groups and Network ACLs Deploy resources in private subnets using Amazon VPC Enable HTTPS using SSL/TLS Encrypt data in S3 and RDS #Aws #AwsService #Microservice #Backend #development

Running separate Prometheus servers for every AWS environment is such a classic "it worked in dev" trap that doesn’t scale in real life. This piece walks through how AWS managed Prometheus metrics collection simplifies monitoring across Amazon EC2, Amazon ECS, and Amazon MSK, so you spend less time babysitting scrapers and more time actually using the data. I especially liked the way it breaks down operational headaches like HA, scaling, and security, and shows how managed options reduce that overhead without dumbing things down. If you’re dealing with metric sprawl across AWS, Nilo Bustani’s article is well worth a read - definitely worth a save for your observability playbook. #AWS #Prometheus #Observability #CloudMonitoring