Docker: What It Is, How It Works, and Essential Commands

Docker is an open-source platform that packages applications and dependencies into containers: lightweight, portable, isolated units that run identically in any environment. Unlike virtual machines, containers share the host kernel and consume far fewer resources.

• What is Docker?
• How does Docker work?
• What is the difference between Docker and virtual machines?
• What are the advantages of Docker?
• First steps with Docker
• Main Docker commands
• Conclusion

What is Docker?

Docker is an open-source platform that lets you create, distribute, and run applications in containers. A container is a lightweight, portable unit that packages all the software needed to run an application — code, libraries, dependencies, and configurations — into a single package that behaves the same on the developer laptop, the test server, and production.

Docker relevance is measurable: according to the Stack Overflow Developer Survey 2025, Docker was the most used development tool of the year, with 71% adoption among respondents — a jump of 17 percentage points over 2024, the largest recorded among all surveyed technologies.

Unlike virtual machines (VMs), which virtualize the entire hardware, Docker containers virtualize only the operating system they run on. This architecture choice makes them lighter, faster to start, and more efficient in CPU (Central Processing Unit), memory, and disk usage.

How does Docker work?

Docker works from three pieces: the image, the container, and the Docker Engine. The image is an immutable template, described in a file called Dockerfile, that defines the base system, dependencies, and application commands. The container is a running instance of that image. The Docker Engine is the service that builds images and manages the container life cycle on the host.

The typical workflow follows this order: the developer writes a Dockerfile, builds the image with docker build, tests locally with docker run, and publishes the image to a registry like Docker Hub, from where any server can download it with docker pull. All interaction happens through the Docker command-line interface (CLI).

This model is the foundation of modern DevOps practices: the same image validated in development is promoted to production without changes, reducing configuration errors across environments.

What is the difference between Docker and virtual machines?

The core difference is the level of virtualization: Docker virtualizes the operating system and shares the host kernel across containers, while a virtual machine emulates the full hardware and loads an entire guest operating system for each instance. That is why containers start in seconds and VMs in minutes.

This lightness explains why containers dominate the cloud. In the CNCF (Cloud Native Computing Foundation) annual survey, 82% of container users ran Kubernetes in production in 2025, up from 66% in 2023 — and the report announcement describes Kubernetes, the container orchestrator, as "the de facto 'operating system' for AI", the de facto operating system for AI (Artificial Intelligence).

What are the advantages of Docker?

Docker offers four main advantages for programmers: portability, isolation, resource efficiency, and speed. Together, they standardize environments, reduce dependency conflicts, and shorten the cycle between writing code and putting it into production.

Portability

Docker containers run anywhere the Docker Engine is installed: on your laptop, in a local data center, or on providers like AWS, Azure, and Google Cloud. This ensures consistency across development, test, and production environments.

Isolation

Each Docker container is isolated from the others: changes in one container do not affect other containers or the host system. Two applications can use different versions of the same library on the same server without conflict.

Resource efficiency

Docker containers share the operating system kernel with the host, so they consume less memory and disk than virtual machines. In practice, the same server runs many more containers than equivalent VMs.

Speed

Docker containers start and stop in seconds, which speeds up local testing, continuous integration and delivery (CI/CD) pipelines, and deploy strategies with fast rollback.

First steps with Docker

To start using Docker in a project, follow these steps:

1. Install Docker on your operating system following the official installation guide, available for Windows, macOS, and Linux.
2. Validate the installation by running docker run hello-world in the terminal — if you are new to the topic, review the fundamental terminal commands first.
3. Pull a ready-made image from Docker Hub with docker pull, such as nginx or postgres, and run a container with docker run.
4. Write a Dockerfile for your application, declaring the base image, dependencies, and startup command.
5. Build and run your image with docker build and docker run, and integrate the process into your CI/CD pipeline when it is stable.

You do not need to master a specific language to operate Docker: the CLI handles day-to-day work. For programmatic automation, the Docker Engine exposes an API with official SDKs in Go and Python.

Main Docker commands

The ten commands below cover the routine of anyone working with containers. The complete reference is in the official Docker CLI documentation.

1. docker run: creates and starts a Docker container from an image.

docker run image_name

2. docker ps: lists running containers on the system. With the -a option, it lists all containers, regardless of state.

docker ps

3. docker stop: stops a running container, identified by ID or name.

docker stop container_id

4. docker start: starts a container that was previously stopped.

docker start container_id

5. docker restart: restarts a running container.

docker restart container_id

6. docker pull: downloads a Docker image from a registry, such as Docker Hub, to the local system.

docker pull image_name

7. docker build: creates a Docker image from a Dockerfile, which contains the build instructions.

docker build -t image_name dockerfile_path

8. docker rm: removes one or more Docker containers, identified by ID or name.

docker rm container_id

9. docker rmi: removes one or more Docker images from the system.

docker rmi image_name

10. docker exec: runs a command inside a running container.

docker exec container_id command

Conclusion

Docker has ceased to be a differentiator and become basic literacy: when 71% of developers use the tool and 82% of container users run Kubernetes in production, not knowing how to containerize an application is what stands out in the market. The CodeCrush practical recommendation is straightforward: install Docker today, containerize a personal project end to end — Dockerfile, build, run — and only then move on to orchestration. This foundation pays dividends in any career involving backend, data, or infrastructure.