Troubleshoot EC2 Connection Issues

🚀 From EC2 to Production-Ready Load Balancer Setup ☁️ Just completed setting up a production-level load balancing architecture on AWS — here’s a structured breakdown of the journey 👇 🔹 1. Security First 🔐 Configured a Security Group with controlled inbound access: ✅ Port 22 – SSH (for admin access) 🌐 Port 80 – HTTP 🔒 Port 443 – HTTPS 👉 Ensuring only required traffic is allowed is the first step toward a secure infra 🔹 2. Scalable Compute Layer ⚙️ 🚀 Launched 3 EC2 instances 🧩 Attached same Security Group for consistency 📜 Used User Data scripts to automate setup 👉 Result: Identical, ready-to-serve application servers 🔹 3. Target Group Setup 🎯 🔗 Created a Target Group (TG) 💓 Configured health checks for reliability 🔄 Registered EC2 instances to TG 👉 Ensures traffic goes only to healthy instances 🔹 4. Load Balancer with High Availability 🌍 ⚖️ Created Application Load Balancer (ALB) 🌐 Enabled Multi-AZ deployment 🔁 Distributes traffic evenly across instances 💥 Provides fault tolerance & zero single point of failure 📸 Live Proof: EC2 instance up & running in ap-south-1a 💪 💡 Key Takeaway: A production-ready setup isn’t just about launching servers — it’s about security, automation, scalability, and resilience working together seamlessly. Brijesh Bapat Rutuja Tandel Vaibhav Kokare Ulhas Narwade (Cloud Messenger☁️📨) Amazon Web Services (AWS) 🔥 Next Step: Add Auto Scaling + Monitoring for a fully automated infra! #AWS #CloudComputing #DevOps #LoadBalancer #EC2 #SystemDesign #CloudArchitecture

𝗔𝗪𝗦 𝗟𝗮𝗺𝗯𝗱𝗮 𝘁𝘂𝗿𝗻𝗲𝗱 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 𝗶𝗻𝘁𝗼 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 At Amazon Web Services (AWS), infrastructure doesn’t always mean servers. Sometimes, it’s just code that runs when needed. That changes how applications are built. Without serverless: • teams manage idle infrastructure • scaling requires planning • costs grow with unused resources With AWS Lambda, teams run 𝗲𝘃𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆. The DevOps lesson: 𝗗𝗼𝗻’𝘁 𝗺𝗮𝗻𝗮𝗴𝗲 𝘀𝗲𝗿𝘃𝗲𝗿𝘀. 𝗠𝗮𝗻𝗮𝗴𝗲 𝗲𝘃𝗲𝗻𝘁𝘀. When compute becomes on-demand, you only pay for what you use. At ServerScribe, we help teams design architectures that scale automatically — without operational overhead. Are you still managing servers — or building serverless systems? 👇 #DevOps #ServerScribe #AWSLambda #Serverless #CloudComputing #SRE #Scalability

Today I worked on creating a volume snapshot in the us-east-1 region using the AWS CLI. This is a common practice for backups or before performing risky system updates. Command used: aws ec2 create-snapshot --volume-id --description "" --tag-specifications 'ResourceType=snapshot,Tags=[{Key=Name,Value=}]' --region us-east-1 Verification: aws ec2 describe-snapshots --snapshot-ids --query "Snapshots[*].State" --region us-east-1 Result: "completed" 🎉 #AWS #EC2 #CloudComputing #DevOps

💸 Saved $1500/month on AWS — without touching application code A recent project had an AWS bill of $4000/month. After a quick audit, it was clear: the problem wasn’t scale… it was waste. Optimized the infrastructure and brought it down to **$2500/month** — with zero downtime and no performance impact ⚙️ Here’s what actually made the difference 👇 🔹 Kubernetes (EKS) Fixes → Corrected pod CPU & memory requests/limits (major over-provisioning) → Improved cluster efficiency instantly 🔹 EC2 Right-Sizing → Replaced oversized instances based on real usage metrics 🔹 RDS Optimization → Tuned DB instance size as per workload → Eliminated unnecessary capacity 🔹 CloudWatch Logs Control → Applied retention policies to stop infinite log storage billing 🔹 Storage Cleanup → Deleted unused EBS volumes & old snapshots → Removed hidden cost leaks 🔹 Smart Scheduling (Dev Environment) → Automated nightly shutdown of EKS + RDS → Pay only when actually in use --- 📉 Impact: ✔️ ~$1500/month saved (~37% reduction) ✔️ Cleaner, efficient infra ✔️ Better cost visibility --- 💡 Most AWS bills are high not because of usage… but because no one is actively optimizing them. If your cloud cost feels higher than expected, there’s a good chance you’re paying for things you don’t even use. #AWS #DevOps #CloudOptimization #FinOps #Kubernetes #EKS #RDS #CloudC

🚀 What is Load Balancer in AWS? After learning Auto Scaling… Let’s understand how traffic is distributed 👇 ⚖️ What is a Load Balancer? A Load Balancer distributes incoming traffic across multiple EC2 instances. 👉 Simple idea: Load Balancer = Traffic distributor 🔹 Why is it important? ⚡ Prevents overload on one server 🔁 Improves availability 📈 Works with Auto Scaling 🔹 How it works: 1️⃣ User sends request 2️⃣ Load Balancer receives traffic 3️⃣ Distributes to multiple servers 🔹 Real-life example: Like a traffic police officer who directs vehicles to different roads 💡 Real Insight: Without Load Balancer… one server may crash under heavy traffic #AWS #LoadBalancer #DevOps #CloudComputing #LearningInPublic

Most people think you need servers to run code… but what if your code runs without managing any server at all? 🤯 That’s exactly what AWS Lambda does. It’s a serverless compute service where you just upload your code, and AWS handles everything else — scaling, infrastructure, and execution. Example: -Imagine you upload a photo to a website. -Instead of running a server 24/7 to process that image: -AWS Lambda automatically triggers -Resizes the image -Stores it in another folder And the best part? You only pay for the time your code runs ⏱️ ⚡ Why it’s powerful: • No server management • Auto scaling (even for millions of requests) • Cost-efficient (pay per execution) • Easy integration with other AWS services 📌 In short: Focus on writing code, not managing servers. #AWS #Lambda #Serverless #CloudComputing #DevOps #LearningInPublic

Excited to share my latest project: Migrating Legacy AWS Infrastructure to Infrastructure as Code using AWS CDK (TypeScript). I took a manually built “ClickOps” environment and redesigned it into a secure, reproducible AWS architecture with a VPC, public/private subnet segmentation, EC2, RDS, Security Groups, and AWS Secrets Manager, all deployed through code. Beyond implementation, I focused on the why behind the architecture decisions, applying system design principles around security, scalability, reliability, and cost optimization. 📖 Medium article —link in the first comment #AWS #AWSCDK #InfrastructureAsCode #DevOps #CloudArchitecture #SystemDesign

Every infrastructure engagement has the same moment. You open the AWS console and find 15 EC2 instances, a dozen S3 buckets, a VPC with security groups nobody fully understands — all created by hand, none of it in Terraform. Getting it under Terraform management is one of the most tedious jobs in infrastructure work. Not because it's hard. Because it's slow. The old way: → Pull the resource details from AWS → Figure out the right Terraform resource type → Write the HCL by hand → Run terraform plan, see a massive diff → Tweak, re-plan, repeat For one resource: 15 minutes. For 50 resources: a full day. Claude Code changes this entirely. You hand it the job, it runs the AWS CLI itself, writes the HCL, runs the plan, reads the diff, fixes it, and loops until the plan is clean — unattended. 25 S3 buckets: 20–30 minutes of Claude Code working while you do something else. By hand: most of a day. Alex Podobnik wrote up exactly how we structure this — the CLAUDE.md setup, the agentic loop, and how to scope bulk imports without losing control of what gets touched. Link in the comments. #Terraform #DevOps #InfrastructureAsCode #AWS #AIEngineering #PlatformEngineering #CloudEngineering

Deployed a scalable web application (FitTrack) using AWS EC2 Auto Scaling. -Created a launch template for instance configuration -Configured Auto Scaling Group (min: 1, max: 2) -Implemented CPU-based scaling using Amazon CloudWatch -Integrated Application Load Balancer -Hosted web page using Apache -Automatic scaling ensures performance during peak load and cost optimization during low usage. #AWS #CloudComputing #EC2 #AutoScaling #DevOps

🚀 Understanding Terraform Made Simple! If you’re getting started with Infrastructure as Code (IaC), this flow makes Terraform super easy to grasp 👇 🔹 Terraform Workflow in 5 Simple Steps: 1️⃣ Write (.tf files) Define your desired infrastructure (servers, networks, storage). 2️⃣ Init (terraform init) Initialize the working directory, download providers, and configure backend. 3️⃣ Plan (terraform plan) Preview what Terraform will create, update, or destroy before making changes. 4️⃣ Apply (terraform apply) Provision or update your infrastructure in real environments (AWS, Azure, GCP, etc.). 5️⃣ Destroy (terraform destroy) Clean up resources when they’re no longer needed. 💡 Key Concept: Terraform uses a state file (.tfstate) to track your infrastructure and ensure consistency between configuration and real-world resources. 🔥 Why Terraform? ✔️ Automates infrastructure ✔️ Ensures consistency ✔️ Enables scalability ✔️ Supports multi-cloud environments 👉 In short: Write → Init → Plan → Apply → Destroy Simple, Powerful, and Reproducible! #DevOps #Terraform #CloudComputing #InfrastructureAsCode #AWS #Azure #GCP #Automation #Kubernetes #Learning