Terraform vs Pulumi vs AWS CDK: My Infrastructure as Code Experience

🚀 Auto Deploy Static Website using AWS Amplify + GitHub This time, I’ve taken it one step further 🔥 Instead of manually uploading files to S3, I demonstrated how to: 👉 Push code to GitHub 👉 Connect it with AWS Amplify 👉 Enable automatic deployment on every code update 🔗 GitHub Repository (Code + Guide): https://lnkd.in/g_mcUKtf 💡 What’s special in this setup? Fully automated CI/CD pipeline ⚡ No manual upload required Every push → Auto build → Auto deploy Real-world production workflow used by companies 🧠 Tech Stack Used: Amazon Web Services AWS Amplify GitHub 📌 What you’ll learn: ✔ Connecting GitHub repo to Amplify ✔ Setting up auto-deployment pipeline ✔ Build & deploy configuration ✔ Continuous integration basics ✔ Production-level hosting workflow 📈 Why this is important? In real companies, developers don’t upload files manually. They push code → pipeline handles everything automatically. 🙏 Special thanks to Ulhas Narwade (Cloud Messenger☁️📨) Sir and Amazon Web Services (AWS) for continuous guidance. Saroj Kumar Chand Rashmi Bhakre Ashlesha Athale Sudarshan Darade Akash kolhe alhad prabhudesai Vasant Mane Ravikala Zilte Madhan G Jaleel Shaik If you want to become a Cloud / DevOps Engineer, this is a MUST skill 💯 💬 Drop your thoughts & let’s connect! #AWS #AWSAmplify #GitHub #CICD #DevOps #CloudComputing #Automation #WebDevelopment #Frontend #Deployment #CloudEngineer #TechIndia #Learning #SoftwareEngineering #BuildInPublic

Stop guessing which tool to use for Infrastructure as Code. Choose the right one for your needs. I was knee-deep in a project, balancing the complexities of multiple cloud environments. The team was split between different opinions—some swore by Terraform, others leaned towards Pulumi, and a few were advocating for AWS CDK. Each had its own merits, but which tool would truly fit our workflow? We were in a sprint when the need for a consistent and efficient IaC solution became glaring. Terraform had its strongholds with a vast community and mature ecosystem, but its HCL syntax felt cumbersome for our fast-paced dev cycles. Pulumi was attractive with its promise of using familiar programming languages, but there was some hesitation around its evolving maturity. CDK, on the other hand, seemed perfect for deep AWS integration, but the lock-in was a concern. I decided to prototype a simple infrastructure setup using each tool to explore their nuances. Contrary to my initial bias, the CDK allowed me to leverage existing TypeScript patterns seamlessly, saving us loads of time in the later stages. Terraform's plan feature was unbeatable for visualizing changes, and Pulumi's language flexibility was perfect for our developers skilled in Python. ```yaml # Sample Terraform setup provider "aws" { region = "us-west-2" } resource "aws_s3_bucket" "my_bucket" { bucket = "my-example-bucket" acl = "private" } ``` The key lesson? Match the tool to your team's strengths and project needs. CDK suited our AWS-central focus, while Terraform was unmatched for multi-cloud. Pulumi fit teams wanting to code infrastructure in their favorite language. Which one do you lean towards in your projects, and why? #DevOps #CloudComputing #Kubernetes #IaC

Today was Day 1 of building my AI-Integrated Media Pipeline on AWS. 🚀 The Win: Deployed a fully automated S3 infrastructure in the Canada (Central) region using Terraform. The Tech: ✅ Infrastructure as Code (Terraform) ✅ Remote State Management (S3 + Native Locking) ✅ Versioned Storage Architecture The Lesson: ✅ Setting up the AWS CLI on WSL (Ubuntu) was a reminder that the "Dev" in DevOps is just as much about environment configuration as it is about writing code. ✅ Hooking up a Python Lambda to trigger AI processing on every upload. #AWS #Terraform #DevOps #CloudEngineering #CanadaTech #BuildingInPublic

⚙️ Terraform Level 2 — No more hardcoding. Ever. In Level 1 I was writing values directly into my code. Bucket name? Hardcoded. Region? Hardcoded. That works for one person, one environment, one time. That's not how real teams work. Level 2 taught me the right way: variables.tf  → define what inputs the code accepts terraform.tfvars → set the actual values per environment outputs.tf   → expose important values after apply Now the same Terraform code works for: → dev environment → staging environment   → production environment Just change the .tfvars file. Nothing else. This is how teams manage infrastructure across environments without duplicating code. GitHub: https://lnkd.in/dfngngsu #Terraform #AWS #DevOps #InfrastructureAsCode #LearningInPublic

Just when you think you’re done provisioning your infrastructure… then terraform validate says otherwise — boom, another error 😮💨 You spend days trying to bring your design to life, fix the obvious errors, clean up the syntax, troubleshoot and You test again. Then just when you think you’re finally done, one more validation error shows up and that part can be exhausting. Every time I build infrastructure, I’m reminded that validation issues, design fixes, and retries are all part of building stronger hands-on experience. One thing I’m learning from this Terraform journey is that growth in tech is not always just about knowing the tools. Sometimes it is about staying calm, stepping back, and trying again when things refuse to work the way you expected. So this is just a reminder to anyone building, learning, debugging, or feeling stuck right now: progress is still progress, even when it looks slow. What do you usually do when you get stuck in the middle of a project? #DevOps #Terraform #AWS #CloudEngineering #InfrastructureAsCode #TechJourney #LearningInPublic #BuildInPublic

How I Structure Terraform Code for Real-World Projects One common interview question: “How do you organize Terraform for large environments?” Over time, I’ve learned that structure matters more than writing code. Here’s the approach that has worked well for me Use reusable modules Instead of duplicating code, create modules for common resources (VNet, App Services, etc.) Separate environments properly Keep Dev / UAT / Prod isolated using separate state files and configurations Remote state management Store state securely (e.g., backend storage) to enable collaboration and avoid conflicts Parameterization over hardcoding Use variables and tfvars to keep code flexible and environment-agnostic Plan before apply Always review changes—never blindly apply in production Keep it simple Over-engineering Terraform can create more problems than it solves 💡 Biggest takeaway: Good Terraform is not just about provisioning infrastructure— it’s about making it repeatable, maintainable, and safe to change. Curious—how do you structure Terraform in your projects? #terraform #devops #cloud #iac #engineering

𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐒𝐭𝐚𝐭𝐞 𝐅𝐢𝐥𝐞: 𝐖𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐢𝐬 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 Most people learning Terraform understand the basics quickly, you write config, you run apply, infrastructure gets created. But the state file? That's where things get interesting. Here's what you actually need to know: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐬𝐭𝐚𝐭𝐞 𝐟𝐢𝐥𝐞 𝐝𝐨𝐞𝐬 Terraform keeps a record of every resource it manages. When you run a plan or apply, it compares that record against your config and the real world infrastructure, and figures out exactly what needs to change. Without it, Terraform has no memory. It wouldn't know what it built or what to touch. 𝐋𝐨𝐜𝐚𝐥 𝐯𝐬 𝐑𝐞𝐦𝐨𝐭𝐞 𝐬𝐭𝐚𝐭𝐞 By default the state file sits on your machine — fine for solo projects, risky for anything else. In a team environment you store it remotely. AWS S3 is the most common option. HCP Terraform (HashiCorp's own platform) is increasingly the recommended choice — it handles versioning, encryption, and locking out of the box. 𝐒𝐭𝐚𝐭𝐞 𝐥𝐨𝐜𝐤𝐢𝐧𝐠 When two people run Terraform at the same time against the same state, things can break badly. State locking prevents this — only one operation can hold the state at a time. DynamoDB handles this when using S3 as your backend. 𝐃𝐫𝐢𝐟𝐭 If someone goes into the console and manually changes something Terraform manages, your state file no longer reflects reality. That gap is called drift — and it's one of the more frustrating things to debug if you don't know what you're looking for. 𝐓𝐡𝐞 𝐠𝐨𝐥𝐝𝐞𝐧 𝐫𝐮𝐥𝐞 Never edit the state file directly. Terraform provides CLI commands for any state manipulation you need. Direct edits can cause Terraform to destroy and recreate resources unexpectedly. The state file isn't the most exciting part of Infrastructure as Code. But misunderstand it, and it will cost you. credit for image CoderCo #Terraform #InfrastructureAsCode #DevOps #CloudEngineering #CloudComputing #Automation

Great breakdown! Taking this a step further, the best way to protect your state and your infrastructure is by moving Terraform execution off local machines entirely and into a CI/CD pipeline. By running terraform plan in CI on a pull request and terraform apply in CD on merge, you fundamentally shift how infrastructure is managed. It attaches the expected state changes directly to the PR, allowing your team to review the exact infrastructure impact before anything is applied. More importantly, if you grant deployment permissions exclusively to the CD server, developers can no longer modify cloud resources or the tfstate locally. Every change must go through version control, enforcing a strict IaC approach and eliminating the need to distribute high-level cloud credentials to individual laptops. The state file is Terraform's memory, but a solid CI/CD pipeline is its bodyguard!

🌆 Still thinking about this one after 5 years in DevOps… Why is there no standard tool that just reads cron expressions in plain English? 🗣️ 0 0 L * * — most engineers I know would Google this 1#2 — second what of the month?? 😭 5,35 8-18/2 * * 1,3,5 — genuinely cursed 💀 So I built cronread.com 🛠️ Paste any expression → plain English + exact next run times + copy-ready snippets for your stack 📋 Supports: 🔹 L W # advanced syntax 🔹 AWS EventBridge 6-field cron 🔹 Terraform • K8s • GitHub Actions • AWS CDK 🔹 Your local timezone auto-detected 🌍 Free. No login. No nonsense. #DevTools #Cron #AWS #DevOps #Terraform #Kubernetes #GitHubActions #CloudEngineering #BackendDev #BuildInPublic #SideProject #CloudComputing #Automation #AWSCDK #SoftwareEngineering #IndieHacker #LinuxAdmin #OpenSource

Stop fighting your Infrastructure-as-Code deployment order! 🏗️ Ever had a Terraform apply fail because a resource was created before its prerequisite was ready? That’s where Dependencies come in. In Terraform, there are two ways to handle the "What comes first?" problem: 🔹 Implicit Dependencies: The "smart" way. Terraform sees Resource A using an ID from Resource B and automatically builds B first. No extra code needed! 🔹 Explicit Dependencies (depends_on): The "manual" way. Sometimes resources are linked logically but not through data. Use this to tell Terraform: "Don't touch Resource A until Resource B is fully finished." Pro-Tip: Lean on Implicit dependencies as much as possible—it keeps your code cleaner and easier to maintain. Save depends_on for those tricky edge cases! How do you handle complex dependencies in your modules? Let’s discuss below! 👇 #Terraform #DevOps #CloudComputing #IaC #AWS #PlatformEngineering