Learn Docker in 2026 - Complete Roadmap Beginner to Pro

[00:00] Docker containers are like perfectly packed suitcases for your code. Everything it needs for the journey is in there. Nothing more, nothing less.

[00:12] But the magic of Docker isn't in the technology. It's when deployment becomes as simple as sharing a recipe. And that's why Docker has become a standard technology in software development and deployment.

[00:25] But for many beginners, it's overwhelming to figure out what to learn first, what to focus on, and how to get started with Docker in a structured way. So in this video, I'll break down exactly what you need to learn about Docker, in which

[00:37] order, and give you a roadmap that will take you from an absolute beginner to confidently using Docker in your projects. Now, Docker isn't just for DevOps and cloud experts.

[00:49] It's for anyone who's ever said, I wish I could just send my entire computer along with my code. So if you want to really dive in with all the resources, I also created a Docker roadmap PDF for this video that

[01:04] visualizes everything we'll cover here plus some additional resources to guide your learning journey. So it's basically a PDF companion to this tutorial. So grab one below and let's get started. The first step is to understand what Docker is solving.

[01:21] So before diving into Docker commands and creating Docker images, you need to understand the problem of it works on my machine and how containers solve this.

[01:33] Let's say you're working on a Node.js application. On your machine, on your local development machine, you have Node version 14 installed, but the production server runs version 16. Your application

[01:47] works perfectly on your local environment, but when it's deployed on the server, it breaks. because of these environment differences. And this is exactly what Docker solves.

[02:00] Docker lets you package your application with its entire environment, dependencies, configurations, everything it needs into a single container that will

[02:12] run exactly the same way anywhere Docker is installed. So Docker solves an important problem, making applications work the same everywhere. On every environment where the application is moved around. Because Docker packages everything application needs

[02:29] the application code itself, the libraries and dependencies into standardized containers. So your application will run the same way on local development, testing, production environments

[02:43] without it works on my machine problem. Now one of the most powerful aspects of Docker is that you don't need to create images for common services yourself. Almost every major software service already has an official Docker image.

[03:00] You need a database for your application. There are official images for MySQL, PostgreSQL, MongoDB, Redis, Elasticsearch, whatever database you need. You need a message broker.

[03:12] You can grab Revit MQ, Kafka images. You need any web servers. and grab an Nginx Apache image, and you're ready to go. And these images are maintained by the software vendors themselves or the Docker community.

[03:27] And all these images are stored in what's called Docker repositories, with Docker Hub being the most popular public repository. So think of Docker Hub as GitHub, but for container images instead of code.

[03:41] So this means that anyone can pull these images and use them in their applications without needing to know how to configure the services from scratch. For example, instead of installing PostgreSQL on your machine, like literally going and downloading

[03:57] the installer or binary and installing it on your operating system and learn how to configure configure the options with the configuration file and dealing with the OS specific issues.

[04:11] As long as you have the Docker installed locally, you can simply pull the postgres URL image and with one simple Docker command, you can have a working database up and running in seconds.

[04:25] This access to ready-made containers drastically reduces the time to get started with any new technology, any new database or message service or whatever service your application needs

[04:38] for local development, but also on the deployment environment without you needing to dig deeper into all these configuration options for each service. And that is an extremely huge value that Docker gives you.

[04:52] So now that we understand value of public Docker images, let's learn the basic commands to actually work with those images to use that. Which means, once you understand conceptually why Docker was created and so widely adopted,

[05:08] you can immediately dive into hands-on practice and start the simplest way with the basic Docker comments to simply take an existing Docker image from the public repository and run it as a container locally on your machine.

[05:23] If you know how to pull an image from Docker repository and start it locally, then access that service, stop and remove containers, you have already mastered the important, the most fundamental basics of Docker in practice. So the first step in

[05:39] your Docker journey is pulling and running existing images and it's as simple as running a few Docker commands. To pull an image you basically have docker pull. With docker pull you can pull an image with any specific version

[05:54] that you want and running an image is as simple as executing docker run command and configuring on which port on your local machine the service should start

[06:06] and the service will be up and running and available and accessible on that specified port. You can also check any running containers that you have, learn how to stop containers, how to remove, how to list images, and these are the most

[06:20] fundamental commands that you'll probably be using like 70 to 80 percent of the time. And I actually recommend starting with a simple public image like Nginx or Redis. They're lightweight and easy to validate and work with, so try pulling it, running it, interacting with it. This will give

[06:37] you hands-on practice with these commands in a real context without needing to build anything yourself yet. And as I said, the step-by-step guide that we are going through right now is in the video

[06:51] handout, so you can directly copy and paste all the commands that I'm showing you from there. You can find the link in the description. Now once you get your very first hands-on practice with Docker,

[07:04] this will complete your fundamental Docker knowledge. And as a next step, we need to understand how to build our own docker images and run them as containers just like any pre-existing image

[07:16] from the repository and it's much easier than you can imagine. I actually recommend taking a simple project like the one that I have linked in the accompanying PDF so you can take that one if you want and just fork the repository. It's a basic web application and you can go ahead and containerize

[07:33] it and this will give you hands-on practice of how to actually build your own image from your own application and run it as a container which will be another important fundamental part of learning Docker concepts in practice Now the way I always approach learning new tools is by defining a simple but realistic use case And it very important that instead of just

[07:58] memorizing commands one by one, take a simple application you already have or the one I linked and try to dockerize it. Through this process alone, which is still very simple, you will

[08:11] naturally learn what each command does and why it's necessary and in which context do you need each one. This type of practical approach actually makes your learning much more effective than if

[08:25] you're just studying from documentation or just blindly copy-pasting some commands without any actual project to practice with. So in order to create your own docker images you need to learn

[08:37] basics of what's called a docker file, which is a blueprint of your docker images. It's like a recipe that you create for docker to build you an image based on that. And this is actually where

[08:50] docker becomes really powerful. Here's an example of a docker file for a Node.js application. Again, you'll find all these code snippets to copy in the handout. And again, this is probably 70% of what

[09:05] makes up any Docker file. You always need the from directive which basically specifies the base image that you are building your own Docker image on. As of that, your application may need

[09:19] Node.js version 14 to run because it's a Node application so you can specify there. You have a Python application that needs Python to run, you take Python image. That's how it works.

[09:31] Then you have a few other docker directories, excepting the current working directory inside the container, copying any files from your host into the container, running any Linux commands inside the container,

[09:44] like npm install or whatever Linux command you can run it all with. Run directive, exposing a port within the container, and running or starting up a service or your application inside the container.

[09:57] And this is the most basic but absolutely practical functioning Dockerfile in order to build your application image. Once you have Dockerfile, with very simple Docker commands you can build and run

[10:09] your own application image using simple Docker commands. Now that you understand the importance of writing your own Dockerfile to create custom container images, you might be wondering, is there

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[11:59] You're going to find the link in the description. Now let's continue with the next step in our Docker roadmap. Now, while Docker files are excellent for defining single containers, of course, real-world applications typically require multiple containers working together.

[12:15] For example, a web application might need a front-end container, a back-end API container, database container, a caching service container, and so on. So you have multiple containers that need to communicate with each other.

[12:27] This brings us to the next crucial concept in your Docker journey, which is Docker networking. So Docker networking allows containers to run in the same virtual network that Docker creates

[12:39] in order to communicate simply with each other using just a container name. So you don't need host name and port number and so on. This is also very important in microservices architecture.

[12:51] For example, if you have a front-end application that needs to talk to a backend API, you can run them in the same Docker network once you create that network so that they can talk to each other in that network. and you can create a new network, a new virtual network in Docker using docker

[13:08] network create command. So in this example the front-end container will be able to communicate with the back-end container using the container name API as the host name because they are in the same container network. Now that you've

[13:23] seen the basic Docker commands and you've played around and learned all this, you will soon notice that typing out these commands over and over again can become repetitive and error-pronged,

[13:37] and in production environment may not be the most optimal to use. This is where the power of Docker Compose comes in, that allows you to define multiple container configurations that should run in the same network in one place in code, rather than through manual commands distributed all around.

[13:56] So Docker Compose provides a simple declarative way to define and orchestrate multi-container applications. This shift from manually running commands to configuration as code, like defining

[14:12] multiple container configurations in one file as code, is part of the fundamental DevOps practice of codifying everything to make things reproducible and consistent. And an example,

[14:26] Docker Compose file will look like this, where we define configuration for all the containers in one file in a very convenient way. So again, you don't need to execute and remember all those

[14:39] comments. You have everything defined in one blueprint. And once you have that, you can run all the containers, so basically the entire configuration that you have defined here, you can run everything with the simple docker-compose up command that will create all the containers

[14:54] that we listed inside with the configuration that we provided. And interesting to note is that Docker Compose also takes care of creating its own network for all the containers that are defined

[15:07] inside, and it also takes care of cleaning up the environment with all the resources when we delete the services or stop them using Docker Compose down command. So with one command we can create

[15:19] multiple services and we can clean up all the services also with one command. Now as our applications grow more complex with multiple interconnected containers you'll face another

[15:31] challenge which is how to ensure data is saved or persisted even when containers are destroyed removed from the server or host

[15:44] and recreated. Like, our applications will need databases, and if we run databases as containers to save data, how do we persist the data? Because the way Docker was designed, it will store the data

[15:58] for a database inside the container's file system, which means when the container is removed and destroyed, the data is gone with the container. It's not persisted anywhere on the actual machine.

[16:15] And containers by nature are ephemeral, which means they need to be very easily replaceable, and you should be able to remove and destroy one very simply and create a new one. They were

[16:27] designed to be temporary. For example, if we need to run a new version of the database, or if we need to recreate database container on another machine, the whole data is gone.

[16:39] And obviously that's not feasible. Docker actually says we manage the environment runtime, all the configuration inside, and you manage the underlying data as you wish. But Docker also gives

[16:53] us an interface to connect this data storage that we manage and take care of to containers in order to have that persistence. And this is where Docker volumes come in, to maintain state in a containerized application

[17:09] and basically persist data outside containers and reattach the reference of the location where data is stored outside the container to the newly created container.

[17:21] So the way it works is following. A volume, a Docker volume, creates a dedicated storage area on your host machine or a storage backend and connects it to a specific location inside your

[17:36] container. So whatever your application writes to that location gets safely stored on the host machine, file system, or that data storage, whatever we configure, in addition to the data written in

[17:50] container. So it's basically near it. Now when your container stops and gets deleted, the data remains safely on the storage, on the host machine. And now when you start a new container,

[18:05] recreate that database container, we can connect that container to the same volume and immediately has access to all the previously saved data. You can think of it as a persistent breach between your

[18:19] temporary container and permanent storage on your host machine. And this is how simple it is in practice to create and use a volume. You can create as many volumes as you want and you can then reference or connect them

[18:33] to the container with an additional parameter like this. But we said that all the configuration should be in code in Docker Compose. And it's equally simple to configure it in Docker Compose as well.

[18:48] by defining volumes and then configuring it for whatever container needs to have access to that volume. So with this configuration, if we removed the database container and recreated a new one with the same volume,

[19:03] our data will actually be saved between those container creations. Our data will still be there. So this already covers the fundamentals of Docker. Like this will actually give you most of the knowledge you need in order to understand conceptually what Docker is,

[19:23] as well as use it in practice in your own projects. This is already enough for most of the basic use cases you're going to have in practice. But there are two more things I want to mention in order to take this level up.

[19:36] Because as you become comfortable with Docker local development, using all these concepts of volumes, Docker Compose, and so on, you will need to start thinking about how to properly prepare your Docker applications for production environments.

[19:52] Building secure, efficient, and maintainable Docker images actually requires following best practices that usually go beyond just getting containers to run. So once you're comfortable with Docker, you can start focusing on learning these production best practices.

[20:10] For example, the most important best practice examples are using specific image text instead of using latest, especially in production. Which means always use exact version text like node 18.17 alpine instead of node latest.

[20:30] Why? Because when you use latest, it can actually change any time without warning. Like when they upload the latest image, which will be different, can change any time without warning.

[20:44] And that could potentially mean that suddenly when you build your Docker image again, or when you run your Docker container, because when a new image is available with the latest tag,

[20:57] and you run your Docker container again, you'll basically get a completely new image and suddenly your perfectly working application that was tested in development environment will crash in production

[21:09] because overnight the base image updated with some breaking changes that your tests never caught. And you always have this risk when you use latest. Another best practice is to combine run commands

[21:22] with the double end. So instead of having multiple run commands stacked one after another. The best practice is to actually chain them together like this, and the reason is because each run directive actually creates a new layer in your image,

[21:40] which adds an extra weight to your container, like actually makes it larger. By combining commands, however, you make your image smaller and faster to download. Plus, you also reduce attack surface

[21:55] because you're not carrying around unnecessary tools and libraries that attackers could exploit. Now in order to create smaller images, another best practice is to use multi-stage builds. You can think of this like pecking for a trip. You don't bring your entire closet when you're

[22:12] going on a trip, right? You only bring exactly what you need for that specific trip. So with multi-stage builds you first use a larger image to build your app because it may have some tools

[22:26] that you need for packaging and building your application. Then you simply copy the finished product to a clean minimal image that doesn't have all these extra tools that we don't need.

[22:41] And you can see in practice this can shrink the image sizes dramatically, sometimes from one gigabyte down to less than 100 megabytes. Smaller images deploy faster, they cost less to

[22:55] store, and also very important they have fewer security issues because the number of tools and the attack surface is just smaller. Another security best practice in production is to not run containers

[23:10] as root. Always add a user and switch to that user. If your container runs as root, it's basically like giving admin privileges to every piece of code inside If an attacker breaks into your container they just get the keys to the kingdom So best practice is to create a dedicated user in your Docker file with simple Linux

[23:34] commands and then use the user directive or instruction to switch to it. This way, if someone exploits your app, they're trapped in a limited sandbox without permission

[23:46] to access the entire host system or other containers in the network. And finally, last security production best practice is to scan your images, and this is an important keyword, regularly.

[23:59] Because your image might be secure today, but it may not be secure tomorrow. Why? Because very simply, new vulnerabilities are constantly discovered for different packages

[24:12] that your image may be using, or specific versions of the dependencies may become vulnerable. And there are tools like Docker Scout that can check your images against a constantly

[24:27] updated database of known security issues. And it's as simple as running Docker Scout command. And you can do this image security scan in your CIC pipeline as well when building images

[24:42] so that you can catch any security issues before they reach production. And again, all these code snippets and the step-by-step guide of what we just covered is in this video handout, so please make sure to download and actually follow this tutorial

[25:00] in order to get started with Docker. Because as soon as you understand the concepts, it's important to dive in and actually try it out hands-on. And the handout actually also includes some additional tips that I won't be able to cover

[25:16] in this video alone, but there will be a super valuable resource for you as you continue your Docker journey. And if you want to thank us for that additional work we put in to make an amazing

[25:28] video handout, to make your learning journey as smooth as possible, then you can share this video with one friend or colleague who you think will benefit from learning Docker as well. However,

[25:40] we're not done yet. There is one important last step missing to upgrade your Docker skills at this point. So after mastering Docker production fast practices, as well as understanding all the

[25:52] important Docker concepts, you will want to integrate Docker into your overall development and employment workflow, right? This means learning how to store your Docker images in registrants and

[26:06] automating the process of building and deploying your Docker images through CRCD pipelines. And this automation is a core principle of DevOps that improves software delivery efficiency,

[26:22] and it also reduces human error because most of the steps are automated and always executed in the same way. So there is less risk of an actual human making any mistakes if there is no human

[26:34] involved, right? So the first step here is learning how to use Docker registries like Docker Hub or AWS ECR, which is another very popular one, or if you're using GitHub Actions, for example,

[26:46] you can use GitHub Container Registry for simplicity, and that will help you integrate Docker in your CRCID pipeline. For example, this is an example, GitHub Actions code snippet

[27:00] that basically builds a Docker image and pushes to a Docker repository using pure Docker commits. So this could be any image pushed to any repository, which automates this step in your pipeline,

[27:15] which means on every commit to your main branch, your CI-CD pipeline can produce a deployable Docker image or an application artifact that can be deployed on a final deployment environment.

[27:29] Now, learning never stops, especially in the IT world with DevOps and cloud basically being non-stop dynamic environment.

[27:41] And I'm going to leave you with one final next step once you've actually learned Docker. In practice, in modern projects, you will most certainly be working with microservices applications, which are a perfect use case for Docker containers.

[27:57] So if you have a large microservices application, you'll have a container per microservice. That's how it works. Plus, you will have containers for the database services and the message brokers and the various

[28:10] other services that your microservices use. And when you deploy them all as containers on a production environment, managing these containers will quickly become just impossible with Docker alone or even Docker Compose.

[28:26] simply because Docker and Docker Compose simply do not solve all the challenges of running containers at scale, like hundreds or thousands of them. And as your containerized applications grow, you will need to think about automatically restarting the failed containers

[28:42] because you can't be manually logging in and restarting hundreds of containers every single week. You need to think about scaling containers up or down based on the load, right? Maybe you want to add 100 replicas of a certain application because it's just getting a lot of

[28:58] traffic. Or rolling updates with zero downtime. Load balancing traffic across container instances. That's another huge topic. And this is exactly where container orchestration platforms like

[29:12] Kubernetes come into play. Because of that, Kubernetes is actually the natural next step after mastering Docker. Kubernetes builds on the foundation of Docker containers,

[29:24] but adds powerful features for managing hundreds or sometimes thousands of containers, as well as hundreds of servers, in order to automatically deploy, scale up and scale down,

[29:37] and just manage these containers. Now, a very important lesson when learning both Docker and Kubernetes is to have patience. Take your time with the foundational concepts and avoid leaving

[29:52] any knowledge gaps because the more knowledge gaps you leave, the harder and more complex it actually will become learning new features and tools and new concepts on top of it. While building

[30:05] on solid foundation makes learning new concepts just much easier and much more enjoyable as you progress because you'll have reference knowledge to relate every new concept or

[30:20] technology or feature to. So it always pays off in the end. Remember that slow patient learning is slow at the beginning but faster in the long run. So I

[30:32] hope this Docker roadmap video gave you a clear path of what you need to learn in which order as well as give you actual code examples and commands and so on to

[30:45] practice the knowledge by simply following this video or the handout that we prepared and I really hope it gives you that confidence of understanding docker and knowing how to use it in practice and build your future knowledge

[31:01] on top of that. Quick let me know if this was valuable for you and how you're planning to use it in practice or at the work or personal projects, I would really love to hear.

[31:13] And with that, as always, thanks for watching and see you in the next video!