For interviews, we can say: "IAM is an AWS service that helps with authentication and authorisation of users, or anyone accessing our AWS account and resources."

We all know about the root user, who has ultimate power and can go anywhere in our account. But if someone new joins your organisation, you don't want this newbie to have all that access. (Tip: protect the root user with MFA and don't use it for daily work.)
Let's suppose this newbie is a junior developer, just like our intern. We want him/her to only read and modify code, not delete existing code. This is where IAM helps. It comes with different building blocks: IAM users, IAM groups and IAM roles, and through them we can provide specific IAM policies and permissions. Let's see where he/she fits best.
IAM user: an identity for a person or an application, with long-term credentials such as a password or access keys.

IAM group: used when specific people belong to the same team and need the same permissions. Rather than attaching policies to each person's account separately, we add them to an existing group or create a new one.

IAM role: meant for temporary access, with temporary credentials. A user, an AWS service or another account can assume the role for a limited time.
In short, a user is one identity with long-term credentials, while a role is temporary access that anyone or anything can assume.

IAM policy: a JSON document that grants or denies permissions. Policies are attached to IAM identities (users, groups or roles).

IAM permissions: the API actions that can or cannot be performed. They are written inside the IAM policy document.

Now we know all the categories, so our newbie can be added to an IAM group that has predefined policies and permissions.
Benefits of IAM
Improves security and reduces the risk of attacks and accidental damage.
More specific control, and easier to manage in the long term.
So, back to our question: the intern goes into a group that can read and modify code but cannot delete it. Even if they click the wrong button, the production database stays safe. This is called the principle of least privilege: give only the access a person needs to do their job.
We have come to the end of this article, and I hope you learned something and enjoyed it. Do share and support my writing. 😊
Next, we will learn about EC2.

Our previous articles showed why the cloud and its services matter. But when everything on the internet sounds so similar, how do we pick what fits? That's where deployment models come in. Broadly, we classify them into 4 categories:

1. Private Cloud : The infrastructure is used by only one organization. We either manage it ourselves or set up the whole thing on our own, in our own data center or hosted privately. It's like owning a house: full control and privacy, but you pay for and maintain everything. Good for: banks and organizations with strict security rules Trade-off: high setup cost and you need a skilled team Examples: Red Hat OpenStack, VMware vSphere

2. Public Cloud : Services are provided to us over the internet by big companies like Google, IBM, Microsoft, and Amazon. We don't own the hardware. We use what we need and pay only for that, like renting. Good for: startups, students, and businesses that want to start fast without big upfront costs Trade-off: less control over the underlying infrastructure Examples: AWS, Microsoft Azure, Google Cloud, IBM Cloud This is the model most of us work on, and it's the one we'll focus on.

3. Hybrid Cloud : A mix of private and public. A company can keep sensitive customer data in its private cloud and run its website on the public cloud, so it gets security and flexibility. When traffic suddenly spikes, it can also borrow extra capacity from the public side. Examples: AWS Outposts, Azure Arc

4. Community Cloud : Shared by several organizations with common needs, such as government departments or hospitals with the same security and compliance rules. They share the infrastructure and the cost. Example: government-focused clouds like AWS GovCloud (US) are often given as examples

Now that we have a solid understanding of these distinct deployment models, in my upcoming articles we will dive deep into learning about the Public Cloud, specifically focusing on AWS services.

We will explore exactly how it actually all works behind the scenes. Yeah, I am very excited let's go!🌸😊

What Does Linux Actually Do?

Linux is a kernel -> a bridge between hardware and software. It lets hardware components like the CPU and RAM communicate directly with software (the OS) and send tasks back and forth efficiently. Linux was first introduced in 1991 by Linus Torvalds. It's open source and free to use, and soon after release, it was adopted by organizations everywhere. From the early days of cloud computing, Linux was used rapidly because of three things: it's lightweight, highly customizable, and scriptable. Windows, on the other hand, is built around a GUI, uses more system resources, and the bigger issue for a company running thousands of servers , it's an entire licensed operating system with a real cost attached. Linux costs nothing.

Kernel vs. OS: The Confusion Everyone Has

People often mix up Linux and an operating system. Here's the actual difference:

Linux is a kernel - a part of an OS that performs tasks by talking directly to hardware.

An OS is the full operational unit of a computer, and it's the OS that executes tasks for the user.

Many operating systems like Ubuntu -are built on top of the Linux kernel. That's the real relationship: Ubuntu isn't a replacement for Linux, it's an OS that uses the Linux kernel underneath it. That's exactly why cloud servers rely on a direct link to hardware for speed and efficiency and Linux is what makes that link possible.

Talking to Linux

Linux gives you commands that connect directly to the file system and let you operate on it: cd, pwd, rm, cp, mkdir, touch, grep, and more. And this is exactly why cloud-native tools like Docker are built for Linux from the ground up: a Docker container shares the host machine's Linux kernel directly, instead of emulating a whole separate machine the way a virtual machine does. That's what makes containers so fast and lightweight compared to VMs. Because of all these capabilities and Linux's uniqueness in giving software direct, efficient access to hardware , it's what the entire cloud runs on.

Next time in Cloud Made Easy, we'll go one level deeper: what's actually the difference between a virtual machine and a container? See you then.

On the internet, every request travels from one device to another. There are two roles: a client — you, sending the request — and a server — the one receiving and responding.

Think of ordering food. You (the client) place an order — that's the request. Thirty minutes later, a delivery person (the server) brings your food — that's the response. The request is asking, the response is getting.

This whole exchange is called the Client-Server Model. Under the hood, it involves how requests actually travel, and which protocols and methods carry them — we'll get there.

This is the exact structure my own project runs on: my frontend sends the request, and my backend on EC2 acts as the server, receiving it and responding back.

Takeaway: every website you visit is just a conversation — client asks, server answers.

Tomorrow: how does your phone know exactly which server, out of millions on the internet, to talk to? That's networking — see you then.

So here's a question I started wondering about:

Where does all that data actually go?

A phone has limited storage. Even a company can't simply keep buying bigger hard drives forever.

So how do platforms handle millions of uploads every single day?

The answer introduces us to one of the most important ideas in cloud computing: scalable cloud storage.

They Don't Store Everything on One Computer

The first thing I had to understand was that Instagram doesn't have one enormous computer sitting somewhere with everyone's photos and videos inside it.

Large applications use distributed infrastructure.

Data can be stored across many physical machines, disks, and data centers. As the amount of data grows, the infrastructure can scale to accommodate it.

This is one of the major advantages of cloud computing.

Instead of building and maintaining all the infrastructure yourself, you can use computing resources provided by cloud platforms such as AWS, Microsoft Azure, and Google Cloud.

Think of it like electricity.

You don't build a power plant in your house just because you need electricity. You use the infrastructure provided by the electricity company and pay according to your usage.

Cloud computing follows a similar idea.

But Where Are the Videos Actually Stored?

This is where cloud storage comes in.

For large amounts of files such as photos, videos, backups, and documents, cloud providers offer object storage.

On AWS, one example is Amazon S3.

Instead of saving a video to the disk of one server and hoping that server is always available, object storage is designed to store huge amounts of data with high durability and scalability.

So when a user uploads a video, the application can process it and store the resulting files in a storage system designed specifically for this kind of workload.

The important idea is:

The application grows, and the storage infrastructure can grow with it.

That's why companies don't suddenly “run out of disk space” every time millions of users upload new content.

So What Exactly Is Cloud Computing?

This is where my original question led me.

Cloud computing is the on-demand delivery of computing resources such as compute, storage, databases, and networking over the internet.

Instead of buying and maintaining all the infrastructure yourself, you can use resources from a cloud provider when you need them.

There are different ways organizations can deploy cloud infrastructure, such as:

• Public cloud — infrastructure provided by a cloud provider and shared among customers.

• Private cloud — cloud infrastructure dedicated to a particular organization.

• Hybrid cloud — a combination of private/on-premises infrastructure and public cloud services.

I'll go deeper into these later. For now, the important thing I wanted to understand was the basic idea:

Cloud = using scalable computing infrastructure without having to own all of the physical infrastructure yourself.

What I Learned Today

I started with a simple question about Instagram and TikTok.

But that question led me to three important concepts:

Compute: machines that run applications
Storage: systems that store data
Scalability: the ability to handle increasing demand by expanding resources

I'm currently learning these concepts through AWS and hands-on projects.

For example, my Notes API backend runs on an AWS EC2 instance. That helped me understand the “compute” side of cloud computing.

Next, I want to understand something even more basic:

When I type a website address into my browser and press Enter, what actually happens to my request?

Day 2 → Client-Server Model