Virtualization : is like having multiple computers inside one physical computer. Each “virtual” computer (called a virtual machine or VM) acts like a separate, independent system with its own operating system and apps. It’s great for keeping things separate and safe, but it can be a bit heavy and slow because each VM needs its own chunk of resources.
Containerization : on the other hand, is like having different apps in separate boxes, but all sharing the same space. Each box (called a container) holds everything an app needs to run, like the app itself and all its tools. It’s lightweight and quick because all the containers share resources and don’t need their own operating systems.
So, while virtualization divides up a computer into separate chunks, containerization apps neatly into separate boxes, making everything more efficient and easier to manage.
difference between containerization and virtualization
Docker🐋 : Think of Docker as a tool that helps you package and run your applications. It uses containers, which are like virtual boxes, to hold everything your app needs to run: the code, tools, libraries, and settings. Docker makes it easy to build these containers, move them around, and run them on any computer that has Docker installed. It’s like a magic box that simplifies the process of getting your app up and running, no matter where you are or what kind of computer you’re using.
Docker hub : Think of Docker Hub like a huge online store where people share their magic boxes (containers) with each other. It’s a place where developers can find ready-made containers for all sorts of software, from databases to web servers.
container: A container is like a virtual box that holds an application and all the things it needs to work properly. It’s a self-contained package that you can move around and run on any computer that has Docker installed.
Deploy image : Deploying an image in the context of Docker means taking a packaged application (which is called an image) and running it on a computer or a server. You’ve prepared your application along with all its necessary files, settings, and dependencies, and you’ve put them all neatly into a box called an image. Now, deploying that image is like taking that box to a location (like a computer or a server) and unpacking it so that your application can start running there.so, deploying an image simply means setting up and starting your application using the resources available on a computer or a server using Docker or a similar containerization platform.
So, Docker helps developers package their apps into containers, Docker Hub is a place to find and share these containers, and deploying an image is like putting your container to work.
Amazon ECS(Elastic Container Service): Amazon ECS is like a virtual playground where you can run and manage your software applications in containers. Think of each container as a small, self-contained package that holds everything your app needs to run smoothly. ECS helps you organize these containers so they work together seamlessly, making it easier to deploy, scale, and manage your applications in the cloud. It’s like having a reliable playground manager that takes care of all the logistics while you focus on playing with your apps.
Launching AWS ECS CLUSTER :
Develop a task definition.
Specify details regarding the Docker image.
Incorporate the service into the Docker ECS cluster by updating the task definition file.
here I have created a cluster with fargate
from the docker hub I have copied the image repository name which we need in the later step of task definition
Making a task plan with the nginxhello image means deciding how the nginxhello container will work within an ECS cluster. The nginxhello image probably has a basic NGINX web server that shows a simple ‘Hello, World!’ page. The task details cover stuff like what’s in the Docker image, how much CPU and memory it needs, networking, and how the containers are defined.
here I have create dtask definition by putting the necessary information of the docker image
then we created a task to run in the cluster I’ve linked the security group to set up rules for traffic coming in and going out of the container. here I have choose the port http and the port range value is 80.
by using the public IP of the created task we can able to view the NGINX web server running on the web.as shown in the above screen capture.
