AWS Lambda Serverless Workflows and Mindset Shift

Day 17 / 30 – AWS Learning Journey ☁️ ✅ What I learned today: - What AWS Lambda is and how serverless architecture works - Key components of serverless computing - Differences between EC2 vs Lambda - When to use server‑based vs serverless architectures - Trade‑offs in cost, scalability, and management 🔍 Key takeaway: Choosing between EC2 and Lambda depends on the use case. Serverless reduces operational overhead, while EC2 offers more control—architecture decisions should be workload‑driven. 📘 Resources: • AWS Zero To Hero Course For DevOps Engineers – YouTube • Official AWS Lambda Documentation 🎯 Next up: Hands‑on cost optimization using AWS Lambda #30DaysOfAWS #AWS #Lambda #Serverless #CloudArchitecture

What is AWS Lambda?🚀 AWS Lambda is a serverless compute service that lets you run code without managing servers. You simply upload your code, and Lambda takes care of scaling, execution, and infrastructure — you only pay for what you use. It’s perfect for building event-driven, scalable applications with minimal operational overhead. 📚 Recently, I completed the course: AWS Lambda - A Practical Guide - Learn from an Expert Big thanks to Daniel Galati for such a clear and practical learning experience! I’ve also compiled my notes from the course here: https://shorturl.at/aT2sm This journey helped me better understand concepts like serverless architecture, event-driven systems, and how Lambda integrates with other AWS services. #AWS #Lambda #Serverless #CloudComputing #Learning #Udemy #DevJourney

🔹 New Video Alert! 🔹 👉 Master advanced Amazon S3 concepts essential for AWS exams & real-world projects! 🔗 https://lnkd.in/gVyE2pAW Explore presigned URLs, versioning, secure sharing, recovery, and more with practical demos! Level up your AWS skills today. Like, share, and comment below 👇 #AWS #AmazonS3 #AWSCertification #CloudComputing #DevOps #LearnAWS #S3Tips #AWSDeveloper #CloudSkills

🚀 New AWS update that can change how we use serverless AWS Lambda can now mount Amazon S3 as a file system. At first, it may look like a small change. But it can bring important improvements. 💡 No need to download and upload files Lambda can work directly with files in S3, like a local folder. 💡 Shared data between functions Many Lambda functions can use the same files at the same time. 💡 Good for AI workflows This is very useful for GenAI: - Agents can share data - Multi-step pipelines become simpler - It is easier to keep state between steps 💡 Simpler architectures We can reduce complexity and avoid extra steps. #AWS #Serverless #Lambda #GenAI #SoftwareEngineering

Completed Day 28 of my AWS learning journey, Where I explored AWS Lambda (Serverless computing) and Route 53 (DNS service). This session helped me understand how automation and domain routing work in cloud environments. Through hands-on practice, I automated tasks using Lambda and configured different routing policies. What I worked on: 🔹 Created an EC2 instance 🔹 Created Lambda function with Python runtime 🔹 Configured IAM role for Lambda 🔹 Wrote code to stop EC2 instance automatically 🔹 Tested Lambda function using events 🔹 Created Route 53 Hosted Zone 🔹 Configured different routing policies 🔹 Tested DNS-based traffic routing Quick Understanding : 🔹 Lambda → Run code without managing servers (serverless) 🔹 IAM Role → Grants permissions to Lambda 🔹 Route 53 → Converts domain name to IP address 🔹 Routing Policies → Controls how traffic is distributed This session helped me understand automation and intelligent traffic routing in AWS. #AWS #Lambda #Route53 #Serverless #CloudAutomation #DNS #HandsOnLearning

AWS New Launch Alert: S3 Files An exciting update from AWS — S3 Files. Now, Amazon S3 buckets can be accessed like a file system, bridging the gap between object storage and traditional file storage. Key Highlights: - Access S3 as a native file system - Supports standard file operations (read, write, update, delete) - Works seamlessly with EC2, ECS, EKS, and Lambda - Enables shared access across multiple compute resources - ~1ms latency for active data using high-performance storage - Automatic synchronization between file system and S3 Why this matters: Earlier, we had to choose between: - S3 → scalable & durable object storage - File systems → interactive access With S3 Files, AWS combines both worlds making architectures simpler and more efficient. Use Cases: - Machine Learning pipelines - Data sharing across clusters - AI/Agent-based systems - Applications needing file-level access to S3 This is a big step towards making S3 the central data hub in cloud architectures. Check out the official AWS blog for more details: https://lnkd.in/gRU3uxJd #AWS #CloudComputing #DevOps #S3 #CloudArchitecture #AWSUpdates

My 30-Day AWS Terraform Challenge. Day 3/30. Inspired by Piyush sachdeva Today I worked on creating an S3 bucket using Terraform and focused on understanding how authentication and resource provisioning come together in AWS. Instead of just writing code, I spent time understanding how Terraform communicates with AWS and how credentials play a foundational role in every deployment. Key Learnings: • Terraform uses AWS credentials to interact with AWS APIs • Authentication can be configured using AWS CLI, environment variables, or profiles • Amazon S3 is an object storage service used for storing files like backups, logs, and application data • S3 bucket names must be globally unique and follow strict naming conventions • Terraform workflow is simple and powerful: init → plan → apply → destroy • Tagging resources helps with organization and cost tracking What I built: • A simple S3 bucket using Terraform • Configured AWS provider with region • Applied infrastructure changes and verified in AWS This day reinforced a key idea: Cloud infrastructure is not just about creating resources, it’s about understanding how systems securely connect and operate together. Building step by step. Full blog here: https://lnkd.in/gYgNXrzr GitHub repo: https://lnkd.in/gmsCBxZi #30DaysOfAwsTerraform #Terraform #DevOps #AWS #CloudEngineering

🚀 AWS Learning Journey – Day 5: AWS Lambda (Serverless Computing) In this session, I learned about AWS Lambda, which is one of the most important services in AWS for running applications without managing servers. ☁️ What is AWS Lambda? AWS Lambda is a serverless compute service where we can run code without provisioning or managing servers. 👉 We just upload code, and AWS takes care of execution, scaling, and infrastructure. 💡 Why Lambda is Important? No need to manage servers Automatically scales based on requests Pay only for execution time ⚙️ How Lambda Works Code runs in the form of functions Functions are triggered by events AWS executes the function when triggered 📌 Triggers (Event-Based Execution) Lambda functions can be triggered by: HTTP requests File uploads Database changes Scheduled events 👉 This makes it event-driven computing. ⚡ Key Concepts Covered 🔹 Serverless Model No server management Focus only on code 🔹 Scaling Automatically handles multiple requests No manual intervention required 🔹 Pricing Charged based on execution time No cost when not running 🔹 Cold Starts (Concept) Slight delay when function runs after being idle 🧠 Key Understanding: AWS Lambda changes the way applications are built by removing infrastructure management and allowing execution of code only when needed. This session helped me understand how modern applications can be built using event-driven and serverless architecture. #AWS #Lambda #Serverless #CloudComputing #LearningJourney #Day5

🚀 AWS Question of the Day — Answer Explained Yesterday’s question was: What is the default limit of S3 bucket storage? A. 5 TB B. 100 TB C. 1 PB D. No Limit ✅ Correct Answer: D. No Limit 💥 The moment I truly understood S3… Early in my AWS journey, I used to think like this: Okay… EC2 has limits, EBS has limits… so S3 must also have some fixed storage cap, right? Wrong. And this misunderstanding can seriously limit how you design systems. 🔥 Why S3 is “Unlimited” Amazon S3 is designed as object storage at a massive scale. 👉 There is no predefined limit on total storage in a bucket. You can store: GBs TBs PBs Even beyond 💡 AWS automatically scales behind the scenes. You don’t provision storage. You don’t manage disks. You don’t worry about capacity. That’s the power of cloud-native design. ⚠️ But here’s where people get confused… There are limits — just not on total bucket size. 👉 Each object can be up to 5 TB 👉 You can have virtually unlimited objects So people often mistake object limit for bucket limit ❌ Why NOT other options? A. 5 TB 👉 This is the max size of a single object, NOT the bucket B. 100 TB 👉 Sounds realistic… but AWS doesn’t cap you here C. 1 PB 👉 Even this is NOT the limit—companies store far more than this in S3 🧠 Simple Rule to Remember: 👉 S3 = Unlimited storage 👉 Only object size has limits (5 TB per object) 🚀 Real-world impact This is why S3 is used for: ✔ Data lakes ✔ Backup & archives ✔ Media storage ✔ Big data workloads Because you never have to worry about: “Will I run out of space?” 💬 Be honest — did you think it had a limit? 😄 And where are you using S3 in your projects? #AWS #CloudComputing #S3 #DevOps #CloudArchitecture #Learning

Building notifications directly inside backend flows works at first, but it does not scale well.⚡ This article shows a cleaner event-driven approach using AWS Amplify, SQS, Lambda, DynamoDB, and SES. 🔔☁️ 👉 Read full article here: https://lnkd.in/gqtVvxE6 #enlear #AWS #AWSAmplify #Serverless #DynamoDB #AWSLambda #AmazonSQS #AmazonSES #RealTimeApps #SystemDesign #CloudArchitecture