Welcome to the Journey of Cloud Engineering

When people look at cloud engineering from the outside, it often appears to be about tools. AWS. Azure. Terraform. Kubernetes. CI/CD. And while these technologies matter, over time you realize something important: Becoming a Cloud & IT Specialist is not about mastering one tool. It is about understanding how technology ecosystems work together. The Journey Usually Starts Small Very few people begin their careers understanding: Distributed systems Identity architecture Automation pipelines AI infrastructure Enterprise ecosystems Most journeys begin with: A server issue A deployment problem A database challenge A networking ticket A user access request Over time, curiosity expands. You begin asking: Why does this scale? Why did this fail? How do systems communicate? How can this be automated? How do enterprises manage complexity? That curiosity drives growth. Cloud Engineering Is No Longer Just Infrastructure Throughou...

Artificial Intelligence feels magical from the outside. You type a prompt. An AI responds instantly. Images are generated. Documents are summarized. Code is written. But behind every AI system lies something enormous: Cloud infrastructure!!! Modern AI and Large Language Models (LLMs) are not just software innovations. They are massive distributed computing systems powered by cloud engineering. AI Is a Cloud Workload At its core, an AI model is still an application. But compared to traditional systems, AI workloads demand: Massive compute power High-speed storage Large-scale networking Distributed processing Continuous data pipelines This is why modern AI growth is deeply connected to cloud platforms. Without cloud infrastructure, today’s AI scale would not be practical. Why AI Needs So Much Compute Traditional applications process: Requests Transactions API calls LLMs process: Billions of parameters Large datasets Parallel computations Tr...

  So far in this journey, we’ve explored: Cloud infrastructure Data platforms Identity and access Enterprise applications Automation and CI/CD Most of these revolve around a common assumption: Cloud is used to host web applications. But that assumption is rapidly changing. Cloud today powers a new class of experiences: Immersive applications; especially Virtual Reality (VR). What Makes VR Applications Different? At first glance, a VR application may seem like just another app. But in reality, it introduces unique challenges: High-performance rendering requirements Large asset sizes (3D models, environments) Real-time interaction Device-specific deployment Continuous updates Unlike traditional apps, VR systems are not just about backend processing. They are about experience delivery . Where the Cloud Fits in VR Cloud plays multiple roles in the VR ecosystem. 1. Application Distribution VR applications are typically distributed through platforms like: Meta Que...

In the previous article, we explored why manual deployments don’t scale and how CI/CD changes the way modern systems are delivered. Now the next logical step is: How do you actually design a CI/CD pipeline? Not just using tools; but designing it in a way that is reliable, scalable, and production-ready. What Is a CI/CD Pipeline in Practice? A CI/CD pipeline is a defined sequence of automated steps that takes your code from: Developer → Repository → Build → Test → Deploy → Production Instead of manual actions, every stage is: Automated Repeatable Version-controlled The Core Stages of a Pipeline Let’s break this into practical components. 1. Source Control (The Starting Point) Everything begins with code stored in a version control system like GitHub or repositories in Azure DevOps . This enables: Collaboration Version tracking Controlled changes through pull requests A pipeline is usually triggered when: Code is pushed A pull request is created A...

So far in this journey, we’ve explored: Cloud infrastructure Data platforms Identity and access Enterprise applications Collaboration systems Now we arrive at a critical realization: Building systems is one thing. Operating them consistently is another. And this is where many teams struggle. The Problem with Manual Deployments In many environments, deployments still look like this: Logging into servers Uploading files manually Running scripts Updating configurations Restarting services It works - until it doesn’t. Manual deployments introduce: ❌ Human error ❌ Inconsistent environments ❌ Downtime risks ❌ Lack of traceability ❌ Slow release cycles As systems grow, this approach becomes fragile and unsustainable. A Simple Question If you had to deploy your application today: Could you do it the exact same way every time? Could someone else repeat it without your help? Could you roll back instantly if something breaks? If the answer is ...

So far in this journey, we’ve explored: Cloud infrastructure Data platforms Enterprise applications Identity and access Now let’s bring it all together into something every organization depends on daily: The modern workplace. Emails, documents, collaboration platforms, file sharing, internal portals; all of these are powered by cloud services. And behind all of this is a critical layer:  Cloud administration!!! The Backbone of the Modern Workplace In today’s organizations, employees don’t just use one system. They interact with multiple platforms: Email systems Document storage Collaboration tools Enterprise applications Internal portals To ensure everything works seamlessly, organizations rely on platforms like: Microsoft Entra ID for identity and access SharePoint for file storage and collaboration These systems form the foundation of secure and productive digital workplaces . Identity as the Control Plane Everything starts with identity. Whe...