

Sarthak Varshney is a Docker Captain, 5x C# Corner MVP, and 2x Alibaba Cloud MVP, with over six years of hands-on experience in the IT industry, specializing in cloud computing, DevOps, and modern application infrastructure. He is an Author and Associate Consultant, known for working extensively with cloud platforms and container-based technologies in real-world environments.
Look, I'm going to be straight with you. When I first started learning Docker, the whole "images versus containers" thing made my brain hurt. Everyone kept using these technical definitions that sounded like they were reading from a manual, and I'd just nod along pretending I got it. But I didn't. Not really.
Then one day, my friend Sarah explained it using blueprints and buildings, and suddenly everything clicked. So that's what I'm going to do for you today. Grab a coffee, get comfortable, and let's demystify this stuff together.
Alright, imagine you're playing SimCity or Minecraft or whatever. You've designed this absolutely perfect house. You've got the layout, the materials list, the color scheme, everything. That design? That's your Docker image. It's the plan, the recipe, the master copy.
Now, when you actually build that house in the game, that physical building you can walk around in? That's your container. And here's the cool part: you can use that same blueprint to build a hundred identical houses in different parts of your city. Each house (container) is independent—if one catches fire, the others are fine. But they all came from the same blueprint (image).
That's literally it. Images are the templates. Containers are the running instances. Mind-blowing in its simplicity, right?
When I first heard "you can run multiple containers from one image," I was like, "okay, cool, but why would I want to?" Then I started working on a real project and it hit me.
Picture this: you've got a web app. On your laptop, it works perfectly. You need to test it, so you spin up a container. Works great. Now you want to show it to your teammate, so they spin up another container from the same image on their machine. Meanwhile, your production server is running three containers from that same image to handle traffic. All identical, all from one image, but all completely separate and independent.
It's like having a cookie cutter. The cutter (image) stays the same, but you can make as many cookies (containers) as you want. And if you mess up one cookie, you just toss it and make another. The cutter's still perfect.
Enough theory. Let's actually do this. Pull up your terminal and type:
docker pull nginx
What you just did is download the nginx image from Docker Hub. Think of Docker Hub as the App Store for Docker images. The nginx image is basically a pre-built web server that people use everywhere.
Now check what images you have:
docker images
You'll see something like:
REPOSITORY TAG IMAGE ID CREATED SIZE
nginx latest a99a39d070bf 2 weeks ago 187MB
See that? That's your blueprint sitting on your hard drive. It's not running, it's not doing anything, it's just... there. Waiting.
Now let's build some houses—I mean, spin up some containers:
docker run -d --name my-first-nginx -p 8080:80 nginx
docker run -d --name my-second-nginx -p 8081:80 nginx
docker run -d --name my-third-nginx -p 8082:80 nginx
Boom. You just created three separate web servers, all from the same image, all running independently on different ports. Open your browser and go to localhost:8080, then localhost:8081, then localhost:8082. Three identical web servers, one image.
Check what's running:
docker ps
You'll see all three containers listed. Each has its own container ID, its own name, its own everything. They're siblings, not clones—they share DNA (the image) but live separate lives.
Want to prove they're independent? Kill one:
docker stop my-second-nginx
docker rm my-second-nginx
Now run docker ps again. Two containers left. The other two don't care—they're still running happily. That's the beauty of it.
Okay, so now you get images versus containers. But images themselves are kind of magical, and understanding how they work will level up your Docker game significantly.
Docker images are layered. Like a cake. Actually, exactly like a cake.
Think about making a layer cake. You start with the bottom layer—maybe a chocolate sponge. That's your base. Then you add a layer of frosting. Then another cake layer. Then more frosting. Each layer builds on top of the previous one, and each layer is its own thing.
Docker images work the same way. Let me show you.
When you create a Dockerfile (which is the recipe for building an image), every instruction creates a new layer. Here's a simple example:
FROM ubuntu:20.04
RUN apt-get update
RUN apt-get install -y python3
RUN apt-get install -y python3-pip
COPY my-app.py /app/
CMD ["python3", "/app/my-app.py"]
Each line here is a layer:
Here's what makes this brilliant: Docker caches these layers. If you build this image, then change my-app.py and rebuild, Docker doesn't redo layers 1-4. It reuses them. It only rebuilds from layer 5 onward. This makes builds crazy fast.
It's like if you made a cake, then decided you wanted different frosting on top. You wouldn't bake a whole new cake—you'd just scrape off the top frosting and add new stuff. Same principle.
Here's where it gets even cooler. Remember how I said each layer is its own thing? Well, Docker is smart about storage.
Let's say you have two images:
Both start with Ubuntu. Both install Python. Docker doesn't store that Ubuntu layer twice. It stores it once and both images point to it. Same with the Python layer. Only the Flask and Django layers are different and stored separately.
It's like two cakes sharing the same bottom layers but having different toppings. You're not buying twice as much cake mix—you're being efficient.
You can see this in action:
docker history nginx
This shows you all the layers in the nginx image. Each layer has a size. Notice some say 0B? Those are instruction layers that don't add files, just metadata. The ones with actual sizes? Those added files to the image.
Mistake #1: Confusing stopped containers with deleted images
I can't tell you how many times I did docker stop and thought I'd deleted everything. Nope. The container still exists, just stopped. It's like your car in the garage with the engine off. To actually remove it:
docker rm container-name
And even then, the image is still there. If you want to delete the image:
docker rmi image-name
Mistake #2: Building images with gigantic layers
I once made a Dockerfile that installed like 50 packages in one RUN command. The layer was massive. Then I realized I only needed 3 of those packages. Had to rebuild the entire thing because that one giant layer wasn't reusable for my other projects.
Better approach: Group related stuff together, but don't go overboard. And put things that change frequently (like your app code) near the end of the Dockerfile so you can leverage caching.
Mistake #3: Not naming containers
When you do docker run without --name, Docker gives your container a random name like "angry_einstein" or "sleepy_tesla." Sounds fun, but try remembering which one is which when you have ten of them running. Always name your containers something meaningful.
Mistake #4: Thinking containers persist data by default
This one bit me hard. I spun up a database container, added a bunch of data, stopped the container, started it again, and... all my data was gone. By default, when a container is removed, everything inside it vanishes. If you need data to persist, you need volumes. That's a whole other conversation, but just know: containers are ephemeral by nature.
Here's how I think about it now, and it's never let me down:
When you run docker pull, you're getting a recipe card from the internet. When you run docker build, you're creating your own recipe card. When you run docker run, you're actually cooking the dish. And when you run docker stop or docker rm, you're throwing out that particular dish—but you still have the recipe to make more.
I was working on a side project, a simple Flask web app. I kept developing on my Mac, pushing code to GitHub, then SSHing into my Linux server to deploy it. Every time, I had to remember: "Okay, what Python version am I using? Which dependencies did I install? What environment variables did I set?"
Then I Dockerized it. Created a Dockerfile with all the dependencies. Built an image. Pushed it to Docker Hub. From that point on, deploying was literally:
docker pull my-username/my-app
docker run -d -p 80:5000 my-username/my-app
Same thing ran on my Mac, my Windows desktop, my Linux server, my friend's computer. Everywhere. Identically. That's when I truly got it. Images guarantee consistency. Containers give you flexibility.
Alright, time to cement this knowledge. Here's your mission, should you choose to accept it:
index.html with whatever content you wantHere's a starter to help you:
FROM nginx:latest
COPY index.html /usr/share/nginx/html/index.html
Build it with:
docker build -t my-custom-nginx .
If you can do this challenge and understand what's happening at each step, you genuinely understand images and containers. Not just theoretically—practically.
Images are templates. Containers are running instances. Images are made of layers that stack like a cake. You can create as many containers as you want from one image. Each container is independent. Layers are cached for efficiency. This system is what makes Docker powerful and practical.
That's it. That's the core of Docker. Everything else builds on this foundation. Once you get this—truly get it—the rest of Docker stops feeling like magic and starts feeling like a tool you actually control.
Now go forth and containerize something. Make mistakes. Break stuff. That's how you learn. And when someone asks you "what's the difference between an image and a container?" you'll be able to explain it without sounding like you're reading from documentation.
Because you actually get it.