Docker: Difference between revisions

Latest revision as of 17:43, 6 July 2025

Docker is an open-source platform designed to automate the deployment, scaling, and management of applications using containerization technology. It enables developers to package applications and their dependencies into a standardized unit called a container, which can then be run consistently across different computing environments. The primary advantage of Docker is its ability to facilitate the creation of lightweight, portable, and reproducible software environments, thereby streamlining the development lifecycle and enhancing operational efficiency.

History

Docker was initially released in March 2013 by Solomon Hykes as an internal project for a company called DotCloud, which later became known as Docker, Inc. The platform drew upon several existing technologies, most notably Linux Containers (LXC), which provided the foundational capabilities for container management. Docker’s introduction coincided with the rise of cloud computing, which highlighted the need for new approaches to application deployment and resource management.

By 2014, Docker gained significant traction in the developer community and the tech industry at large. The platform's popularity surged due to its simplicity, robust functionality, and the ability to integrate seamlessly with existing tools and workflows. The open-source nature of Docker allowed developers to contribute to its ecosystem, leading to rapid advancements and the introduction of features such as Docker Compose and Docker Swarm for orchestration and clustering.

In 2016, Docker launched the Docker Enterprise Edition (EE), a commercially supported version of the platform that included enhanced security features and management capabilities geared towards enterprise deployment. This release reflected Docker’s commitment to scaling its technology for larger organizations and integrating it with existing enterprise software infrastructures.

In recent years, Docker has become a core component of DevOps and cloud-native architectures, paving the way for microservices-based application designs and shifting how organizations approach application development and deployment across environments.

Architecture

Docker's architecture is comprised of several key components that work together to provide a comprehensive platform for container management.

Core Components

At the heart of Docker’s architecture is the Docker Engine, a client-server application that contains a server daemon, REST API, and a command-line interface (CLI):

The Docker daemon, or dockerd, is responsible for managing Docker containers, images, networks, and volumes. It handles commands received from the Docker CLI or REST API, performing the necessary actions to create, run, and manage containers.
The Docker client provides a user interface for developers to command the Docker daemon. This component allows for direct communication using commands such as `docker run`, `docker build`, and `docker pull`.
The REST API serves as an intermediary that enables programs and tools to interact with Docker. It allows other applications to automate Docker-related tasks programmatically.

Containerization

The principle of containerization lies at the core of Docker’s functionality, enabling applications to run in isolated environments. Containers share the same operating system kernel but are packaged with their own libraries, configuration files, and dependencies. This approach offers numerous advantages over traditional virtual machines, including reduced overhead, increased start-up speed, and greater resource efficiency.

Each container operates independently, which allows developers to test and deploy software in environments that closely mirror production settings without the risk of interference from other applications or processes running on the host system.

Docker Images

Docker images are the standalone, executable packages that include everything required to run a piece of software—including the code, runtime, system tools, libraries, and settings. Images serve as the blueprint for containers. They are built using a layered filesystem approach, where each instruction in the Dockerfile creates a new layer, making the images lightweight and efficient. When a container is created from an image, only the changes made to that container are saved as a new layer. This layering mechanism facilitates faster downloads, storage efficiency, and easier updates.

Docker Hub is the default registry where users can find and share container images. It contains a vast library of official images maintained by Docker, as well as private repositories for custom images.

Implementation

Docker can be implemented across various environments, from local development machines to large-scale production setups in cloud services. The process is generally straightforward, involving the installation of the Docker Engine, the configuration of container images, and orchestration for managing multiple containers.

Local Development

For local development, Docker enables developers to create isolated environments for testing code without polluting their development setups. By running applications in containers, developers can ensure consistent behavior across different environments. This is particularly beneficial when working on systems that have differing dependencies or configurations.

Developers can utilize Docker Compose, a tool for defining and running multi-container applications. By specifying configurations in a docker-compose.yml file, teams can automate the building and provisioning of entire application stacks, making it easier to manage complex application architectures.

Continuous Integration and Continuous Deployment (CI/CD)

Docker plays a critical role in modern CI/CD workflows. Many CI/CD tools, such as Jenkins, GitLab CI, and CircleCI, support Docker natively, allowing developers to build, test, and deploy applications in an automated fashion. This integration allows for consistent testing environments, thereby reducing the likelihood of issues arising from discrepancies between testing and production environments.

Additionally, containers can be used to run integration tests, ensuring that software components function as expected before deployment. As a result, organizations that use Docker as part of their CI/CD pipelines benefit from faster feedback loops and higher software quality.

Orchestration

As applications grow in complexity and scale, managing multiple containers becomes a necessity. Container orchestration platforms, such as Kubernetes, Docker Swarm, and Apache Mesos, provide the tools required for deploying and managing clusters of containers across a distributed environment. These platforms enable automated load balancing, service discovery, scaling, and self-healing features, which are essential for maintaining high availability and optimal performance in production systems.

Docker Swarm is integrated into Docker and provides native orchestration capabilities, allowing users to create and manage a swarm of Docker nodes easily. Kubernetes, on the other hand, has become the de facto standard for container orchestration, offering extensions and robust community support for more complex deployments.

Applications

Docker's versatility lends itself to a wide variety of applications across diverse industries, transforming traditional software development and deployment methodologies.

Microservices Architecture

One of the most significant applications of Docker is in the implementation of microservices architectures. In a microservices framework, applications are decomposed into smaller, independent services, each responsible for a specific function. Docker containers provide an ideal environment for deploying these services, facilitating rapid iteration and deployment of individual components without affecting the entire application. This modularity results in improved scalability, maintainability, and ease of updates.

DevOps Practices

Docker is a cornerstone of the DevOps movement, which seeks to unify software development and IT operations. By leveraging Docker, organizations can increase collaboration between development and operations teams, enable better communication, and streamline processes. Automated container deployments simplify the management of production environments and allow for continuous monitoring and feedback, improving the reliability and speed of software delivery.

Cloud Computing

The rise of cloud computing has further propelled Docker's adoption, as organizations migrate their operations to cloud-based platforms. Solutions offered by major cloud providers, such as AWS, Microsoft Azure, and Google Cloud Platform, facilitate the deployment and management of Docker containers at scale. These platforms provide services that simplify container orchestration, storage, and networking, making it easier for organizations to integrate Docker into their cloud environments.

Docker's lightweight nature and portability ensure that applications can be run in any cloud environment, offering valuable flexibility for organizations to choose their infrastructure without vendor lock-in.

Criticism

Despite its popularity and numerous advantages, Docker has faced criticism and limitations that organizations must consider when integrating container technology into their workflows.

Security Concerns

One of the primary concerns with Docker containers is their security implications. As containers share the host operating system kernel, vulnerabilities in that kernel can expose all containers to potential threats. Additionally, containers often run with elevated privileges, which can increase the risk of unauthorized access or abuse.

To mitigate these concerns, best practices must be followed, including using minimal base images, regularly updating containers with security patches, and implementing strict access controls. Organizations must also consider employing specialized tools for container security, such as image scanning and runtime protection solutions.

Performance Overhead

Although containers generally offer better performance than traditional virtualization solutions, there can still be performance overhead associated with running multiple containerized applications. Resource contention can occur when multiple containers compete for limited CPU, memory, and I/O resources, potentially leading to degraded application performance. Proper monitoring and resource management strategies are essential to address these issues and ensure optimal operation of containerized environments.

Complexity in Management

While Docker provides substantial benefits in terms of agility and scalability, the management of containerized environments—especially at scale—can become complex. The introduction of orchestration tools can add layers of complexity, requiring organizations to invest time and resources in learning and maintaining these systems. Inadequate knowledge and experience can hinder effective implementation, and organizations may need to seek dedicated training for their staff to maximize the value of Docker technologies.

Conclusion

Docker has transformed the landscape of application development and deployment by providing powerful tools for containerization and orchestration. Its advantages, including portability, consistency, and efficiency, have made it a vital component of modern software practices. Although challenges remain, particularly in areas such as security and management, the continued evolution of the Docker ecosystem reflects the growing importance of container technologies in an increasingly cloud-centric and DevOps-oriented world.

References

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-'''Docker''' is an open-source platform designed to automate the deployment, scaling, and management of applications through containerization. By packaging software into standardized units called containers, Docker enables developers to create applications that can run consistently across different computing environments, making it an essential tool in modern software development, especially in cloud computing and DevOps practices.
+'''Docker''' is an open-source platform designed to automate the deployment, scaling, and management of applications using containerization technology. It enables developers to package applications and their dependencies into a standardized unit called a container, which can then be run consistently across different computing environments. The primary advantage of Docker is its ability to facilitate the creation of lightweight, portable, and reproducible software environments, thereby streamlining the development lifecycle and enhancing operational efficiency.
 == History ==
-Docker was initially released in March 2013 by Solomon Hykes as a project hosted on GitHub. It originated within the confines of a company called dotCloud, which later transformed into Docker, Inc. Docker’s popularity surged rapidly due to its innovative approach to application deployment, leveraging container technology that was new to many developers. The project's early development took inspiration from existing container technologies, such as LXC (Linux Containers) and cgroups (control groups), which allowed the development of isolated environments for applications.
-In June 2014, Docker held its first user conference, DockerCon, which fostered community engagement and gave developers insight into the expanding scope of Docker capabilities. As the project evolved, the development team introduced tools and features such as Docker Compose, which simplified managing multi-container applications, and Docker Swarm, an orchestration tool that enabled clustering and management of Docker nodes.
+Docker was initially released in March 2013 by Solomon Hykes as an internal project for a company called DotCloud, which later became known as Docker, Inc. The platform drew upon several existing technologies, most notably Linux Containers (LXC), which provided the foundational capabilities for container management. Docker’s introduction coincided with the rise of cloud computing, which highlighted the need for new approaches to application deployment and resource management.
-By 2015, Docker had transformed from a simple containerization tool into a robust platform, facilitating a thriving ecosystem around microservices architectures. The introduction of the Docker Hub, which served as a repository for Docker images, further stimulated community participation and allowed developers to share and access container images easily.
+By 2014, Docker gained significant traction in the developer community and the tech industry at large. The platform's popularity surged due to its simplicity, robust functionality, and the ability to integrate seamlessly with existing tools and workflows. The open-source nature of Docker allowed developers to contribute to its ecosystem, leading to rapid advancements and the introduction of features such as Docker Compose and Docker Swarm for orchestration and clustering.
-As of 2021, Docker has continued to evolve through community contributions and enhanced features, including integration with Kubernetes for container orchestration, reflecting the platform's significance in the cloud-native landscape.
+In 2016, Docker launched the Docker Enterprise Edition (EE), a commercially supported version of the platform that included enhanced security features and management capabilities geared towards enterprise deployment. This release reflected Docker’s commitment to scaling its technology for larger organizations and integrating it with existing enterprise software infrastructures.
+In recent years, Docker has become a core component of DevOps and cloud-native architectures, paving the way for microservices-based application designs and shifting how organizations approach application development and deployment across environments.
 == Architecture ==
-Docker's architecture is composed of several fundamental components that enable the creation, deployment, and management of containerized applications. Understanding this architecture is vital for grasping the power of Docker in application lifecycle management.
-=== Components ===
+Docker's architecture is comprised of several key components that work together to provide a comprehensive platform for container management.
-The architecture of Docker consists of a Docker Engine, a REST API, and a graphical user interface known as Docker Desktop. The Docker Engine includes two main parts: the server and the client. The server is responsible for running the containers and managing the images, while the client enables users to communicate with the server to execute commands.
-The Docker Engine can operate in two modes: client-server and as a daemon. In client-server mode, users submit commands through the Docker client, which communicates with the Docker daemon to execute the command. The daemon is responsible for managing Docker containers, images, networks, and volumes, and performs operations requested by the client.
+=== Core Components ===
-=== Images and Containers ===
+At the heart of Docker’s architecture is the Docker Engine, a client-server application that contains a server daemon, REST API, and a command-line interface (CLI):
-Docker utilizes images and containers as core elements. A Docker image is a read-only template with all the necessary instructions for creating a container. Images contain the application code, libraries, dependencies, and runtime needed for the application to function effectively. Containers, on the other hand, are the running instances of these images. Each container is isolated from one another and the underlying host system, ensuring that applications can operate in a consistent environment regardless of where they are deployed.
+* The Docker daemon, or ''dockerd'', is responsible for managing Docker containers, images, networks, and volumes. It handles commands received from the Docker CLI or REST API, performing the necessary actions to create, run, and manage containers.
+* The Docker client provides a user interface for developers to command the Docker daemon. This component allows for direct communication using commands such as `docker run`, `docker build`, and `docker pull`.
+* The REST API serves as an intermediary that enables programs and tools to interact with Docker. It allows other applications to automate Docker-related tasks programmatically.
-The layered file system employed by Docker images allows for efficient storage and versioning of application components. Each Docker image consists of layers stacked on top of one another, with modifications resulting in the creation of new layers while retaining the underlying base image. This mechanism facilitates the rapid distribution and sharing of images while conserving storage space.
+=== Containerization ===
-=== Networking and Volumes ===
+The principle of containerization lies at the core of Docker’s functionality, enabling applications to run in isolated environments. Containers share the same operating system kernel but are packaged with their own libraries, configuration files, and dependencies. This approach offers numerous advantages over traditional virtual machines, including reduced overhead, increased start-up speed, and greater resource efficiency.
-Docker networking allows containers to communicate with one another and with external applications. By default, Docker creates a bridge network for containers to communicate, but users can also create custom network configurations. Docker supports several networking drivers, including bridge, host, overlay, and macvlan, enabling diverse networking capabilities that cater to different application requirements.
-Volumes in Docker facilitate persistent data storage by enabling data to exist independently from the lifecycle of containers. Unlike writable containers created from images, volumes are managed by Docker and can share data across multiple containers. This helps maintain data integrity even when containers are stopped or destroyed.
+Each container operates independently, which allows developers to test and deploy software in environments that closely mirror production settings without the risk of interference from other applications or processes running on the host system.
+=== Docker Images ===
+Docker images are the standalone, executable packages that include everything required to run a piece of software—including the code, runtime, system tools, libraries, and settings. Images serve as the blueprint for containers. They are built using a layered filesystem approach, where each instruction in the Dockerfile creates a new layer, making the images lightweight and efficient. When a container is created from an image, only the changes made to that container are saved as a new layer. This layering mechanism facilitates faster downloads, storage efficiency, and easier updates.
+Docker Hub is the default registry where users can find and share container images. It contains a vast library of official images maintained by Docker, as well as private repositories for custom images.
 == Implementation ==
-The implementation of Docker in software development and deployment has revolutionized how organizations approach building and managing applications. The process of integrating Docker into existing workflows involves several key strategies and tools.
-=== Development Environment ===
+Docker can be implemented across various environments, from local development machines to large-scale production setups in cloud services. The process is generally straightforward, involving the installation of the Docker Engine, the configuration of container images, and orchestration for managing multiple containers.
-Developers can leverage Docker to create consistent and reproducible development environments, eliminating the "it works on my machine" problem that often arises in software development. By using Docker Compose, developers can define multi-container applications through a single YAML file, streamlining the setup of complex development environments necessary for modern applications.
+=== Local Development ===
+For local development, Docker enables developers to create isolated environments for testing code without polluting their development setups. By running applications in containers, developers can ensure consistent behavior across different environments. This is particularly beneficial when working on systems that have differing dependencies or configurations.
-The integration of Docker in the continuous integration and continuous deployment (CI/CD) pipeline is a notable implementation. CI/CD tools, such as Jenkins or GitLab CI, can utilize Docker containers to run tests and build artifacts within isolated environments. This ensures that code is tested in a consistent environment prior to deployment, significantly reducing the likelihood of errors during rollout.
+Developers can utilize Docker Compose, a tool for defining and running multi-container applications. By specifying configurations in a ''docker-compose.yml'' file, teams can automate the building and provisioning of entire application stacks, making it easier to manage complex application architectures.
-=== Deployment Strategies ===
+=== Continuous Integration and Continuous Deployment (CI/CD) ===
-Docker supports various deployment strategies that can optimize performance and uptime. One common approach is the use of microservices architecture, wherein applications are decomposed into smaller, manageable services running independently in containers. This allows for services to be deployed and scaled independently, enabling businesses to respond quickly to changing demands.
-Another popular strategy is blue-green deployment, which maintains two identical environments (blue and green) where one serves live production traffic while the other is idle. In this approach, new versions of applications can be tested in the green environment before switching traffic from the blue environment, thereby minimizing downtime and risk.
+Docker plays a critical role in modern CI/CD workflows. Many CI/CD tools, such as Jenkins, GitLab CI, and CircleCI, support Docker natively, allowing developers to build, test, and deploy applications in an automated fashion. This integration allows for consistent testing environments, thereby reducing the likelihood of issues arising from discrepancies between testing and production environments.
-=== Orchestration with Kubernetes ===
+Additionally, containers can be used to run integration tests, ensuring that software components function as expected before deployment. As a result, organizations that use Docker as part of their CI/CD pipelines benefit from faster feedback loops and higher software quality.
-As applications grow in scale and complexity, managing multiple containers requires orchestration tools. Docker Swarm provides built-in orchestration capabilities, allowing users to manage a cluster of Docker hosts seamlessly. However, Kubernetes, an open-source orchestration platform, has gained significant traction in the Docker ecosystem due to its robust features and extensive community support.
-Kubernetes manages containerized applications across a cluster of machines, offering features such as automatic scaling, load balancing, and self-healing, which enhance the reliability and efficiency of containerized applications. The integration of Docker with Kubernetes allows for a comprehensive solution that addresses the challenges of deploying, monitoring, and maintaining containerized applications in production.
+=== Orchestration ===
+As applications grow in complexity and scale, managing multiple containers becomes a necessity. Container orchestration platforms, such as Kubernetes, Docker Swarm, and Apache Mesos, provide the tools required for deploying and managing clusters of containers across a distributed environment. These platforms enable automated load balancing, service discovery, scaling, and self-healing features, which are essential for maintaining high availability and optimal performance in production systems.
+Docker Swarm is integrated into Docker and provides native orchestration capabilities, allowing users to create and manage a swarm of Docker nodes easily. Kubernetes, on the other hand, has become the de facto standard for container orchestration, offering extensions and robust community support for more complex deployments.
 == Applications ==
-Docker’s versatile architecture has enabled its adoption across various industries and use cases, transforming traditional practices in software development and IT operations.
-=== Web Development ===
+Docker's versatility lends itself to a wide variety of applications across diverse industries, transforming traditional software development and deployment methodologies.
-In the realm of web development, Docker has emerged as a powerful tool for streamlining the development and deployment pipelines. By containerizing applications, developers can ensure consistency between local development environments and production environments, leading to smoother deployments and reduced friction when integrating various components of a web application.
-Moreover, Docker facilitates iterative development through rapid prototyping, enabling developers to build and test new features without affecting the overall application stability. This capability streamlines collaboration in agile teams, promoting a culture of continuous improvement.
+=== Microservices Architecture ===
-=== Data Science and Machine Learning ===
+One of the most significant applications of Docker is in the implementation of microservices architectures. In a microservices framework, applications are decomposed into smaller, independent services, each responsible for a specific function. Docker containers provide an ideal environment for deploying these services, facilitating rapid iteration and deployment of individual components without affecting the entire application. This modularity results in improved scalability, maintainability, and ease of updates.
-In data science and machine learning, Docker containers are leveraged to package dependencies, libraries, and datasets into environments that are easy to deploy and reproduce. Data scientists can share their research easily by providing container images that include all the necessary dependencies, ensuring that colleagues can run the same analyses without compatibility issues.
-Additionally, the ability to spin up and tear down containers allows data scientists to experiment with different model configurations and workflows efficiently. This flexibility can expedite the research cycle and fosters greater innovation.
+=== DevOps Practices ===
-=== Microservices and API Development ===
+Docker is a cornerstone of the DevOps movement, which seeks to unify software development and IT operations. By leveraging Docker, organizations can increase collaboration between development and operations teams, enable better communication, and streamline processes. Automated container deployments simplify the management of production environments and allow for continuous monitoring and feedback, improving the reliability and speed of software delivery.
-Docker is a popular choice for developing microservices architectures, where applications are composed of multiple interdependent services. Each microservice can be developed, deployed, and scaled independently in its container, facilitating faster iteration and deployment cycles.
-API development also benefits from Docker's ability to encapsulate service endpoints within containers. Developers can easily manage versions of APIs, run integration tests, and simulate various response scenarios in isolated environments, resulting in more robust and reliable APIs.
+=== Cloud Computing ===
-=== Education and Training ===
+The rise of cloud computing has further propelled Docker's adoption, as organizations migrate their operations to cloud-based platforms. Solutions offered by major cloud providers, such as AWS, Microsoft Azure, and Google Cloud Platform, facilitate the deployment and management of Docker containers at scale. These platforms provide services that simplify container orchestration, storage, and networking, making it easier for organizations to integrate Docker into their cloud environments.
-Docker has become an invaluable educational tool in training developers and IT professionals. By providing a consistent and repeatable environment, learners can focus on mastering programming languages, development frameworks, or DevOps practices without the burden of setting up environments manually. Educational institutions and online courses often incorporate Docker into their curricula to prepare students for modern software practices.
-== Criticism and Limitations ==
+Docker's lightweight nature and portability ensure that applications can be run in any cloud environment, offering valuable flexibility for organizations to choose their infrastructure without vendor lock-in.
-Despite its widespread adoption, Docker is not without its criticism and limitations. While the technology has revolutionized many aspects of software development, users and experts have identified several areas of concern.
-=== Complexity and Learning Curve ===
+== Criticism ==
-For organizations new to containerization, the initial setup and configuration of Docker can be complex. Understanding the underlying architecture, networking, and storage concepts may pose challenges for teams transitioning from traditional virtualization or monolithic architectures. Additionally, the myriad configurations and settings can overwhelm new users, potentially leading to misconfigurations or security vulnerabilities.
+Despite its popularity and numerous advantages, Docker has faced criticism and limitations that organizations must consider when integrating container technology into their workflows.
+=== Security Concerns ===
+One of the primary concerns with Docker containers is their security implications. As containers share the host operating system kernel, vulnerabilities in that kernel can expose all containers to potential threats. Additionally, containers often run with elevated privileges, which can increase the risk of unauthorized access or abuse.
+To mitigate these concerns, best practices must be followed, including using minimal base images, regularly updating containers with security patches, and implementing strict access controls. Organizations must also consider employing specialized tools for container security, such as image scanning and runtime protection solutions.
 === Performance Overhead ===
-Though containerization offers many advantages, it is essential to note that running applications in containers can introduce performance overhead. Containers share the same kernel and resources of the host machine, which may affect performance compared to native execution. For specific workloads, users may experience a degradation in performance, especially in scenarios requiring heavy I/O operations.
-=== Security Concerns ===
+Although containers generally offer better performance than traditional virtualization solutions, there can still be performance overhead associated with running multiple containerized applications. Resource contention can occur when multiple containers compete for limited CPU, memory, and I/O resources, potentially leading to degraded application performance. Proper monitoring and resource management strategies are essential to address these issues and ensure optimal operation of containerized environments.
-Docker containers inherently share the kernel of the host operating system, leading to potential security vulnerabilities if not properly managed. If a malicious actor gains access to a container, they may exploit their way into the host system. Implementing security best practices, such as running containers with limited privileges and employing Docker security scanning tools, is crucial to mitigate these risks.
+=== Complexity in Management ===
+While Docker provides substantial benefits in terms of agility and scalability, the management of containerized environments—especially at scale—can become complex. The introduction of orchestration tools can add layers of complexity, requiring organizations to invest time and resources in learning and maintaining these systems. Inadequate knowledge and experience can hinder effective implementation, and organizations may need to seek dedicated training for their staff to maximize the value of Docker technologies.
+== Conclusion ==
-=== Vendor Lock-in ===
+Docker has transformed the landscape of application development and deployment by providing powerful tools for containerization and orchestration. Its advantages, including portability, consistency, and efficiency, have made it a vital component of modern software practices. Although challenges remain, particularly in areas such as security and management, the continued evolution of the Docker ecosystem reflects the growing importance of container technologies in an increasingly cloud-centric and DevOps-oriented world.
-Another point of contention is the potential for vendor lock-in associated with container orchestration services. As organizations adopt orchestration platforms like Kubernetes, they may find themselves reliant on specific service providers, creating challenges when migrating workloads to other platforms. This can limit flexibility and increase operational costs if businesses need to scale across different environments.
 == See also ==
-* [[Containerization]]
+* [[Container (virtualization)]]
+* [[Microservices]]
 * [[Kubernetes]]
-* [[Microservices]]
 * [[DevOps]]
-* [[Continuous Integration and Continuous Deployment]]
+* [[Continuous Integration]]
-* [[Cloud Computing]]
 == References ==
-* [https://www.docker.com/ Docker Official Website]
+* [https://www.docker.com Docker Official Site]
-* [https://docs.docker.com/ Docker Documentation]
+* [https://docs.docker.com Docker Documentation]
-* [https://www.docker.com/learn/ Docker Learning Resources]
+* [https://hub.docker.com Docker Hub]
-* [https://www.docker.com/company/ Docker Company Information]
-* [https://www.docker.com/community/ Docker Community]
 [[Category:Software]]
-[[Category:Virtualization]]
+[[Category:DevOps]]
 [[Category:Containerization]]