标签：#cloudcomputing

A practical roadmap for students who want to build real AWS skills, create an impressive portfolio, and prepare for cloud engineering careers. Introduction Every year, thousands of students begin learning AWS. They watch training videos, collect certificates, complete online courses, and share digital badges on social media. Yet when internship interviews or entry-level cloud engineering opportunities arrive, many struggle to answer a simple question: "What have you actually built on AWS?" The cloud industry rewards practical experience, not passive learning. AWS itself focuses beginner learning on hands-on experience with foundational services such as Amazon S3, Amazon EC2, Amazon VPC, Amazon RDS, and cloud security because these services power most real-world cloud environments. If you're a student aiming for a career in Cloud Engineering, DevOps, Site Reliability Engineering (SRE), Solutions Architecture, or Platform Engineering, this article provides a practical roadmap that can help you become job-ready. Why Students Should Learn AWS Cloud computing has become the backbone of modern technology. Companies of every size use cloud platforms to: Host applications Store data Deploy AI workloads Build scalable systems Reduce infrastructure costs Improve reliability As a result, companies continue to hire professionals with cloud skills across software engineering, cybersecurity, DevOps, networking, and data engineering domains. AWS offers dedicated learning paths, hands-on labs, certification tracks, and career-focused programs specifically designed to help learners develop these skills. The key question is not: "Which AWS service should I memorize?" The better question is: "Can I design, deploy, secure, and troubleshoot cloud solutions?" The 5 AWS Projects Every Student Should Build Instead of completing another course, build these five projects. Project 1: Host a Static Website Using Amazon S3 What You'll Learn Cloud Storage Static Website Hosting Bucket Policies O

✍️ This post was written with two hands. The story — the first part — is Murilo's, lived and told by the person who was there. The technical manual , at the end, was written with AI. The split is intentional and marked in the text. Nothing hidden about the seam: part is human, part is machine, and the reader sees both. If you've ever managed or logged into a web server and never set up SSH keys, it's because you don't yet know the real risks of a break-in — and that's okay. Until you find out what can happen. Logging into a server over SSH with a username and password is like locking the front door of a house that faces the street, with nobody keeping watch. Anyone can try as many combinations as they want, freely. And setting this up takes almost as much time as typing a username and password — and it makes getting into the server much faster and easier afterwards. Ignorant of best practices, I managed my servers for a long time by typing: ssh user@server-ip password That nearly cost me dearly, the day I found out my server had been broken into. After that incident, I realized just how vulnerable a username and password are on SSH. Today I can't say I sleep soundly — no system is completely break-in proof — but I sleep a lot better (and honestly, I always slept well, until I started managing servers). Waking up on a fine Sunday morning to do some maintenance on the server, and finding out it was broken into through the front door because you left a combination padlock facing the street — that is not the kind of surprise I'd wish on anyone. I have a degree in Law. I worked for 15 years in the legal field at a public institution, until I decided to venture into the world of programming. And where did I end up? Managing systems at the institution I work for, after spending some time building automations in Python. Managing systems wasn't exactly what I had in mind when I wanted to learn to code and understand the world of programming. But that opportunity ended up tea

I didn’t set out to diagnose hyperscalers. I wasn’t doing a grand industry analysis. I wasn’t mapping global architecture. I wasn’t trying to understand cloud strategy. I was just trying to use a popular software provider — and everything kept breaking. Every time something failed, I followed the thread. And every thread led to the same architectural gap. Eventually I realised I hadn’t been analysing hyperscalers at all. I’d accidentally mapped the substrate failure across the entire industry. Once you see the pattern, you can’t unsee it. Across Microsoft, AWS, Google, and Meta, the same structural drift appears: meaning drift identity drift trust drift state drift execution drift provenance drift agentic drift Different companies. Different stacks. Different histories. Same substrate gap. And it’s not just me. The world is waking up to these problems too. Vendor lock in isn’t just a technical nuisance anymore — it’s becoming a public conversation. People are asking why their money keeps disappearing into the same handful of providers. Organisations are asking why their systems collapse the moment they try to leave. Governments are asking why critical infrastructure depends on architectures they cannot inspect, cannot govern, and cannot reproduce. What started as a personal frustration with a popular software provider turns out to be the same structural issue everyone else is now discovering. And sovereignty is entering the conversation — not as a political slogan, but as an architectural question. When national systems depend on fragmented substrates owned by a tiny cluster of vendors, sovereignty becomes a structural issue. The question isn’t “who controls the cloud?” It’s “who controls the substrate the cloud is built on?” Follow the thread far enough and you reach a scenario nobody wants to think about: what happens in a moment of global stress when a hyperscaler’s fragmented substrate becomes a single point of failure? Not a political crisis — a structural one.

As software engineers, we often architect solutions in a virtual ideal: fast networks, elastic resources, and servers that never physically degrade. But what happens when your carefully crafted systems need to interact with the messy, unpredictable physical world? Think factory floor monitors, real estate camera networks, or remote tracking devices. Suddenly, those cloud assumptions about infinite uptime and perfect connectivity crumble. My journey, particularly architecting and maintaining a continuous 24/7 camera livestream for a real estate group over six years, has been a masterclass in this reality. It's revealed that true reliability in the physical realm demands a hybrid approach – one that intelligently merges the power of edge computing with the scalability and data insights of the cloud. This isn't just about connecting devices; it's about building resilience into the very fabric of your architecture. In this article, I'll share the battle-tested strategies and design principles that enable systems to not just survive, but thrive, despite the harsh realities of physical deployment. 1. The Core Strategy: Smart Edge, Simple Cloud One of the most common pitfalls in hybrid architecture design is treating the edge device as a mere 'dumb' terminal, solely responsible for streaming raw data to a powerful cloud backend. This approach creates a critical single point of failure: if the network drops, the entire system grinds to a halt. Instead, I advocate for a Smart Edge, Simple Cloud architecture. This principle establishes a clear division of responsibility: The Edge : This is where the magic happens locally. The edge system should be robust enough to handle local processing , data filtering , buffering , and immediate hardware control . Critically, it must be capable of operating autonomously for extended periods without an active cloud connection. Think of it as a mini data center, designed for self-sufficiency. Benefits of a Smart Edge : Reduced bandwidth cost

If you've ever spun up an EC2 instance for a side project, accessed a remote work desktop from your personal laptop, or stored files on Google Drive without thinking about the physical hard drive it lives on, you've used virtualization. As the foundational technology behind all modern cloud computing, virtualization transformed how we build, deploy, and manage IT infrastructure—cutting hardware costs significantly for enterprises and making on-demand scalability a reality for teams of all sizes. In this guide, we'll break down exactly what virtualization is, how it powers the cloud, the 6 core types of virtualization, and best practices to implement it safely and efficiently. Table of Contents What is Virtualization in Cloud Computing? Core Virtualization Concepts You Need to Know Role of Virtualization in Cloud Computing 6 Key Types of Virtualization (With Use Cases) Top Benefits of Virtualization for Teams of All Sizes Virtualization vs. Related Technologies Virtualization vs. Cloud Computing Virtualization vs. Containerization Common Virtualization Challenges and Mitigations Real-World Virtualization Use Cases Virtualization Best Practices Conclusion References What is Virtualization in Cloud Computing? Virtualization is a technology that creates virtual, software-based representations of physical hardware (servers, storage, networks, etc.) and abstracts these resources from the underlying physical machine. A software layer called a hypervisor separates operating systems and applications from physical hardware, allowing multiple isolated, self-contained systems called Virtual Machines (VMs) to run simultaneously on a single physical host. Each VM has its own virtual CPU, memory, storage, and network interface, and operates independently of other VMs on the same host. For cloud providers, this technology is the backbone of all on-demand infrastructure services, allowing them to share physical hardware across thousands of customers securely and efficiently. Core Vi

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

Most people understand that a Compute Savings Plan saves money on cloud compute. Far fewer understand the precise mechanism which matters, because getting the commitment amount wrong in either direction costs real money. Too high: you pay for committed hours you do not use. Too low: you miss savings on usage that could have been covered. The difference between a well-sized Savings Plan and a poorly-sized one can easily be tens of thousands of dollars per year on a mid-size fleet. This guide walks through the exact mechanics, hour by hour, with worked examples on both AWS and Azure. Step 1: You Choose a Commitment Amount Before anything else, you decide how much per hour you want to commit. This is the single most important decision in the entire process. Everything else is automatic, the discount application, the coverage calculation, the billing. The commitment amount is a dollar figure: $X per hour. It represents a minimum spend level. You are telling the cloud provider: every hour for the next 1 year (or 3 years), I guarantee I will use at least this much compute. The right commitment amount is your stable baseline, not your average and not your peak. Pull your last 30 days of hourly compute spend. Sort the values. Find the P70 or P75: the spend level you are at or above for 70–75% of hours. That is roughly where your commitment should sit. Why P70–P75 and not the average? Because the average includes your peak hours and your quietest hours equally. If you commit to the average, you generate wasted commitment in the bottom 50% of hours. At P70, you are paying for unused commitment in only 30% of hours and those hours only waste the difference between actual usage and committed amount, not the full committed amount. If you want to understand how commitment-based discounts work across AWS, Azure, and GCP, we covered the full landscape here What Are Commitment-Based Discounts in Multi-Cloud Services? Step 2: The Cloud Provider Applies Discounted Rates Once you have

Introduction In my previous IAM article we learnt basics of IAM and how to create Users, Groups and attach Policies. You can refer here: https://dev.to/kadhamvj23/aws-identity-and-access-management-explained-for-beginners-cn7 After setting up secure access to our AWS account using IAM, the next question we mostly have is where do we actually run our application? The answer is Amazon EC2 - Elastic Cloud Compute. EC2 is one of the most widely used AWS services and understanding it well is essential for anyone starting their cloud journey. In this article we will cover what EC2 is, why it exists, the different types of instances, pricing models, Regions and availability Zones and finally hands-on walk through of creating your first EC2 instance. Breaking Down the Name -EC2 Let us understand what each word in the name actually means: Elastic --> In AWS you will notice many services have this prefix "Elastic". The reason is simple. Whenever AWS provides a service that can be scaled up or scaled down based on our needs, that service is called Elastic . With EC2 you can increase resources when traffic is high and decrease them when the traffic is low. So in simple terms EC2 = A virtual server on the cloud that you can resize anytime. Cloud: EC2 runs on AWS's public cloud infrastructure, meaning the servers are owned and managed by Amazon across the world. Compute: The word compute means you are asking AWS to provide you CPU, RAM and Disk - basically a virtual machine or server that can run your applications. How does EC2 actually work? When you request a Virtual server from AWS, here is what happens behind the scenes: You request a virtual machine on AWS ⬇️ request goes to a Hypervisor(a software layer sitting on top of physical servers that creates and manages VMs) ⬇️ Hypervisor creates your VM ⬇️ You get the access to your EC2 instance You never touch any physical hardware. AWS manages all of that for you. Why use EC2? Imagine your company wants to host an application. T