AWS Cheat Sheet: Core 20 Services for Cloud & DevOps Engineers

🚀 What is AWS Lambda? (In simple terms) Imagine running your code without worrying about servers at all 🤯 That’s exactly what AWS Lambda does. 👉 It is a serverless compute service where: You don’t manage servers You don’t worry about scaling You only pay for what you use 💡 How it works: An event triggers your function → Lambda runs your code → returns the result 📌 Real-world example: When a user uploads an image to S3 → Lambda automatically resizes it → stores the optimized image 🔥 Why developers love it: ✔ No infrastructure management ✔ Auto scaling ✔ Cost-efficient ✔ Easy integration with AWS services 👉 Focus on your logic, AWS handles the rest. 💭 Once you understand Lambda, you’ll never look at backend the same way again. #AWS #Lambda #Serverless #CloudComputing #DevOps #TechSimplified #LearningInPublic

Most engineers think serverless just means Lambda. It is much bigger than that. *What serverless actually means No server management. Not physical. Not virtual. AWS handles provisioning, patching, and scaling. Auto scaling without configuration. No autoscaling groups. No launch templates. Zero to thousands of concurrent executions automatically. Pay for what you use. In idle time there is no cost. An idle EC2 instance still bills you. Serverless does not. Inherently highly available. AWS distributes execution across multiple AZs automatically. No single point of failure. No configuration required. *The core serverless services on AWS Lambda — event-driven compute. Pay per millisecond. Scales to zero. Scales to thousands. No intervention needed. S3 — serverless object storage. Eleven nines of durability. You store. AWS handles the rest. DynamoDB — serverless NoSQL database. On-demand mode means you pay per request. Zero cost when idle. SNS and SQS — serverless messaging. No brokers to manage. Absorbs traffic spikes automatically. EventBridge — serverless event bus. Routes events across AWS services, SaaS tools, and your apps. The backbone of event-driven architecture on AWS. The one that is partially serverless *AWS Fargate. Fargate removes EC2 from the picture. No underlying instances to manage. Bills per task — pay as you go. But a running Fargate task bills you even when idle. You still define CPU and memory per container. Fargate sits between EC2 and true serverless. Use it when your workload needs containers but you do not want to manage EC2. Serverless is not one service. It is a set of properties. No server management. Automatic scaling. Pay as you go. Built-in high availability. When a service meets all four, it is serverless. When it meets some, it sits in between. Choose based on properties, not familiarity. Which serverless service do you reach for first and why? #AWS #Serverless #AWSLambda #DynamoDB #CloudArchitecture #CloudEngineering #SolutionsArchitect #AmazonWebServices #EventDriven #Fargate #S3 #CloudComputing

Day 2 – AWS Compute: When NOT to use what? Yesterday, we explored when to use compute services. Today, we shift our focus to the other side of the coin. Knowing when NOT to use a service is crucial for any architect. ⚙️ EC2 – When NOT to use: ❌ For short-lived or event-driven workloads ❌ When you don’t want to manage servers ❌ For unpredictable traffic (scaling delay) 👉 Why: Requires provisioning, patching, and scaling management ⚡ Lambda – When NOT to use: ❌ Long-running tasks (>15 minutes) ❌ Heavy CPU/memory workloads ❌ Applications needing persistent connections 👉 Why: Execution limits, cold starts, and stateless nature 🐳 ECS – When NOT to use: ❌ If you need Kubernetes standard ❌ Very small/simple apps (overkill) ❌ If you want zero infrastructure management (use Fargate instead) 👉 Why: Still requires cluster and scaling decisions ☸️ EKS – When NOT to use: ❌ Small teams or beginners ❌ Simple applications ❌ When Kubernetes expertise is missing 👉 Why: High complexity and operational overhead 🚀 Fargate – When NOT to use: ❌ Need deep control over infrastructure ❌ Cost-sensitive long-running workloads ❌ Specialized compute requirements (GPU, custom OS tuning) 👉 Why: Higher cost compared to EC2 and less control 🌱 Elastic Beanstalk – When NOT to use: ❌ Complex microservices architecture ❌ Need full infrastructure customization ❌ Advanced DevOps pipelines 👉 Why: Abstracts infrastructure but limits flexibility 📦 AWS Batch – When NOT to use: ❌ Real-time processing systems ❌ Low-latency APIs ❌ Event-driven microservices 👉 Why: Designed for batch jobs, not real-time ⚖️ Architect Mindset: If it’s simple → avoid complex services (EKS, ECS) If it’s event-driven → avoid EC2 If it’s long-running → avoid Lambda If it’s cost-sensitive → evaluate Fargate carefully 🧠 Golden Rule 👉 “Just because you CAN use a service doesn’t mean you SHOULD.” More coming tomorrow 🔥 Next: Storage – When to use S3, EBS, EFS 👉 I’m planning to deep dive into each of these services with real-world architectures. 💬 Which AWS Compute service do you want me to cover in detail next? Drop it in the comments 👇 EC2 Lambda ECS EKS Fargate #AWS #CloudComputing #SolutionArchitect #SystemDesign #DevOps #LearningInPublic

🚀 Just built and deployed my first fully serverless notification system on AWS! This project uses: ✅ AWS Lambda ✅ API Gateway ✅ DynamoDB ✅ SNS ✅ AWS SAM (Infrastructure as Code) 🔧 What it does: A user sends a POST request → API Gateway → Lambda processes it → stores data in DynamoDB → publishes notification via SNS. 💡 What I learned: - How to design event-driven architectures - How to deploy serverless apps using AWS SAM - How to connect multiple AWS services into a real system 📸 I’ve added screenshots + architecture diagram in the repo. 🔗 GitHub Repo: https://lnkd.in/dDCZw-gb This is part of my journey into Cloud Engineering ☁️ More projects coming soon 🚀 #AWS #CloudComputing #Serverless #DevOps #100DaysOfCode

I’ve been learning AWS core services and wanted to put together a simple breakdown with real-time examples. In this video, I covered the key areas that are actually used in most projects: Compute (EC2, Lambda), Storage (S3, EBS), Database (RDS, DynamoDB), Networking (VPC, CloudFront), and Security (IAM). Instead of just theory, I tried to explain how these are used in real-world scenarios like websites, applications, and company infrastructure. This helped me understand how everything connects in cloud environments, especially from a job perspective in Cloud and DevOps roles. If you’re starting with AWS or trying to get job-ready, this might be useful. Here’s the video: https://lnkd.in/gpBuBvEk Open to feedback and suggestions. #AWS #CloudComputing #DevOps #AWSCertification #CloudCareers #Learning

𝗦𝘁𝗼𝗽 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝘄𝗶𝘁𝗵 𝟮𝟬𝟬+ 𝗔𝗪𝗦 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀. 🛑 You don’t need all of them. You only need the right foundation. Most engineers get stuck in “AWS overload” — trying to learn everything → mastering nothing. But real-world systems are built on just a few core layers: 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Where your code runs → EC2, Lambda, ECS 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Where your data lives → S3, RDS, DynamoDB 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴: How services connect → VPC, Route 53, Load Balancer 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Who can access what → IAM, KMS Master these → you’re already ahead of most beginners. Whether you're preparing for AWS exams or building projects, focus on depth over breadth. 💬 Which AWS service do you use the most daily? #AWS #DevOps #CloudComputing #SoftwareEngineering #Learning

𝗦𝘁𝗼𝗽 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝘄𝗶𝘁𝗵 𝟮𝟬𝟬+ 𝗔𝗪𝗦 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀. 🛑 You don’t need all of them. You only need the right foundation. Most engineers get stuck in “AWS overload” — trying to learn everything → mastering nothing. But real-world systems are built on just a few core layers: 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Where your code runs → EC2, Lambda, ECS 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Where your data lives → S3, RDS, DynamoDB 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴: How services connect → VPC, Route 53, Load Balancer 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Who can access what → IAM, KMS Master these → you’re already ahead of most beginners. Whether you're preparing for AWS exams or building projects, focus on depth over breadth. 💬 Which AWS service do you use the most daily? #AWS #DevOps #CloudComputing #SoftwareEngineering #Learning

The sneakiest AWS problems usually aren’t about performance - they’re the quiet cost leaks that torch your budget when you’re not looking. This piece breaks down five common AWS mistakes developers run into, especially around misconfigured resources and “set it and forget it” choices that quietly pile up charges. I liked how it stays practical and focused on what you’d actually check in a real project, not theory. If you care about not getting a nasty AWS bill later, the article by Fozia Saleem is well worth a read. #AWS #CloudComputing #DevOps #CostOptimization

🚀 Just published a new hands-on AWS project! I redesigned a basic cloud setup into a scalable multi-tier architecture using Terraform. Key components include: • Application Load Balancer • EC2 Auto Scaling • RDS MySQL • S3 + CloudFront • Multi-AZ networking • Modular Terraform design 📖 Medium article —link in the first comment Feedback is always welcome! #AWS #Terraform #DevOps #CloudArchitecture

🌐 Deploy AWS ALB with Terraform Learn how to automate AWS Application Load Balancer (ALB) deployment using Terraform. This guide walks you through setting up VPC, EC2, target groups, and security groups as code, eliminating manual configuration for faster, repeatable, and reliable infrastructure. 📖 Check out the full guide: https://lnkd.in/g--wtA-p #AWS #AWSALB #Terraform #DevOps #CloudComputing