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This article explores two popular technologies for abstracting physical hardware: virtualization and containerization. We will provide a general overview of each and also discuss the differences between virtualization and containerization. What Is Virtualization The core component of this technology is the virtual machine (VM). A VM is an isolated software environment that emulates the hardware of a specific platform. In other words, a VM is an abstraction that allows a single physical server to be transformed into multiple virtual ones. Creating a VM makes sense when you need to manage all operating system kernel settings. This avoids kernel conflicts with hardware, supports more features than a specific OS build might provide, and allows you to optimize and install systems with a modified kernel. What Is Containerization Containers work differently: to install and run a container platform, a pre-installed operating system kernel is required (this can also be on a virtual OS). The OS allocates system resources for the containers that provide a fully configured environment for deploying applications. Like virtual machines, containers can be easily moved between servers and provide a certain level of isolation. However, to deploy them successfully, it’s sufficient for the base kernel (e.g., Linux, Windows, or macOS) to match — the specific OS version doesn’t matter. Thus, containers serve as a bridge between the system kernel layer and the application layer. What Is the Difference Between Containerization and Virtualization Some, especially IT beginners, often frame it as "virtualization vs containerization." But these technologies shouldn't be pitted against each other — they actually complement one another. Let’s examine how they differ and where they overlap by looking at how both technologies perform specific functions. Isolation and Security Virtualization makes it possible to fully isolate a VM from the rest of the server, including other VMs. Therefore, VMs are useful when you need to separate your applications from others located on the same servers or within the same cluster. VMs also increase the level of network security. Containerization provides a certain level of isolation, too, but containers are not as robust when it comes to boundary security compared to VMs. However, solutions exist that allow individual containers to be isolated within VMs — one such solution is Hyper-V. Working with the Operating System A VM is essentially a full-fledged OS with its own kernel, which is convenient but imposes high demands on hardware resources (RAM, storage, CPU). Containerization uses only a small fraction of system resources, especially with adapted containers. When forming images in a hypervisor, the minimal necessary software environment is created to ensure the container runs on an OS with a particular kernel. Thus, containerization is much more resource-efficient. OS Updates With virtualization, you have to download and install OS updates on each VM. To install a new OS version, you need to update the VM — in some cases, even create a new one. This consumes a significant amount of time, especially when many virtual machines are deployed. With containers, the situation is similar. First, you modify a file (called a Dockerfile) that contains information about the image. You change the lines that specify the OS version. Then the image is rebuilt and pushed to a registry. But that’s not all: the image must then be redeployed. To do this, you use orchestrators — platforms for managing and scaling containers. Orchestration tools (the most well-known are Kubernetes and Docker Swarm) allow automation of these procedures, but developers must install and learn them first. Deployment Mechanisms To deploy a single VM, Windows (or Linux) tools will suffice, as will the previously mentioned Hyper-V. But if you have two or more VMs, it’s more convenient to use solutions like PowerShell. Single containers are deployed from images via a hypervisor (such as Docker), but for mass deployment, orchestration platforms are essential. So in terms of deployment mechanisms, virtualization and containerization are similar: different tools are used depending on how many entities are being deployed. Data Storage Features With virtualization, VHDs are used when organizing local storage for a single VM. If there are multiple VMs or servers, the SMB protocol is used for shared file access. Hypervisors for containers have their own storage tools. For example, Docker has a local Registry repository that lets you create private storage and track image versions. There is also the public Docker Hub repository, which is used for integration with GitHub. Orchestration platforms offer similar tools: for instance, Kubernetes can set up file storage using Azure’s infrastructure. Load Balancing To balance the load between VMs, they are moved between servers or even clusters, selecting the one with the best fault tolerance. Containers are balanced differently. They can’t be moved per se, but orchestrators provide automatic starting or stopping of individual containers or whole groups. This enables flexible load distribution between cluster nodes. Fault Tolerance Faults are also handled in similar ways. If an individual VM fails, it’s not difficult to transfer that VM to another server and restart the OS there. If there’s an issue with the server hosting the containerization platform, containers can be quickly recreated on another server using the orchestrator. Pros and Cons of Virtualization Advantages: Reliable isolation. Logical VM isolation means failures in one VM don’t affect the others on the same server. VMs also offer a good level of network security: if one VM is compromised, its isolation prevents infection of others. Resource optimization. Several VMs can be deployed on one server, saving on purchasing additional hardware. This also facilitates the creation of clusters in data centers. Flexibility and load balancing. VMs are easily transferred, making it simpler to boost cluster performance and maintain systems. VMs can also be copied and restored from backups. Furthermore, different VMs can run different OSs, and the kernel can be any type — Linux, Windows, or macOS — all on the same server. Disadvantages: Resource consumption. VMs can be several gigabytes in size and consume significant CPU power. There are also limits on how many VMs can run on a single server. Sluggishness. Deployment time depends on how "heavy" the VM is. More importantly, VMs are not well-suited to scaling. Using VMs for short-term computing tasks is usually not worthwhile. Licensing issues. Although licensing is less relevant for Russian developers, you still need to consider OS and software licensing costs when deploying VMs — and these can add up significantly in a large infrastructure. Pros and Cons of Containerization Advantages: Minimal resource use. Since all containers share the same OS kernel, much less hardware is needed than with virtual machines. This means you can create far more containers on the same system. Performance. Small image sizes mean containers are deployed and destroyed much faster than virtual machines. This makes containers ideal for developers handling short-term tasks and dynamic scaling. Immutable images. Unlike virtual machines, container images are immutable. This allows the launch of any number of identical containers, simplifying testing. Updating containers is also easy — a new image with updated contents is created on the container platform. Disadvantages: Compatibility issues. Containers created in one hypervisor (like Docker) may not work elsewhere. Problems also arise with orchestrators: for example, Docker Swarm may not work properly with OpenShift, unlike Kubernetes. Developers need to carefully choose their tools. Limited lifecycle. While persistent container storage is possible, special tools (like Docker Data Volumes) are required. Otherwise, once a container is deleted, all its data disappears. You must plan ahead for data backup. Application size. Containers are designed for microservices and app components. Heavy containers, such as full-featured enterprise software, can cause deployment and performance issues. Conclusion Having explored the features of virtualization and containerization, we can draw a logical conclusion: each technology is suited to different tasks. Containers are fast and efficient, use minimal hardware resources, and are ideal for developers working with microservices architecture and application components. Virtual machines are full-fledged OS environments, suitable for secure corporate software deployment. Therefore, these technologies do not compete — they complement each other.

Docker containerization offers many benefits, one of which is image layering, enabling fast container generation. However, containers have limitations — for instance, persistent data needs careful planning, as all data within a container is lost when it's destroyed. In this article, we’ll look at how to solve this issue using Docker’s native solution called Docker Volumes, which allows the creation of persistent Docker container storage. What Happens to Data Written Inside a Container To begin, let’s open a shell inside a container using the following command: docker run -it --rm busybox Now let’s try writing some data to the container: echo "Hostman" > /tmp/data cat /tmp/data Hostman We can see that the data is written, but where exactly? If you're familiar with Docker, you might know that images are structured like onions — layers stacked on top of each other, with the final layer finalizing the image. Each layer can only be written once and becomes read-only afterward. When a container is created, Docker adds another layer for handling write operations. Since container lifespans are limited, all data disappears once the container is gone. This can be a serious problem if the container holds valuable information. To solve this, Docker provides a solution called Docker Volumes. Let’s look at what it is and how it works. Docker Volumes Docker Volumes provide developers with persistent storage for containers. This tool decouples data from the container’s lifecycle, allowing access to container data at any time. As a result, data written inside containers remains available even after the container is destroyed, and it can be reused by other containers. This is a useful solution for sharing data between Docker containers and also enables new containers to connect to the existing storage. How Docker Volumes Work A directory is created on the server and then mounted into one or more containers. This directory is independent because it is not included in the Docker image layer structure, which allows it to bypass the read-only restriction of the image layers for containers that include such a directory. To create a volume, use the following command: docker volume create Now, let’s check its location using: docker volume inspect volume_name The volume name usually consists of a long alphanumeric string. In response, Docker will display information such as the time the volume was created and other metadata, including the Mountpoint. This line shows the path to the volume. To view the data stored in the volume, simply open the specified directory. There are also other ways to create a Docker Volume. For example, the -v option can be added directly during container startup, allowing you to create a volume on the fly: docker run -it --rm -v newdata:/data busybox Let’s break down what’s happening here: The -v argument follows a specific syntax, indicated by the colon right after the volume name (in this case, we chose a very creative name, newdata). After the colon, the mount path inside the container is specified. Now, you can write data to this path, for example: echo "Cloud" > /data/cloud Data written this way can easily be found at the mount path. As seen in the example above, the volume name is not arbitrary — it matches the name we provided using -v. However, Docker Volumes also allow for randomly generated names, which are always unique to each host. If you’re assigning names manually, make sure they are also unique. Now, run the command: docker volume ls If the volume appears in the list, it means any number of other containers can use it. To test this, you can run: docker run -it --rm -v newdata:/data busybox Then write something to the volume. Next, start another container using the exact same command and you’ll see that the data is still there and accessible — meaning it can be reused. Docker Volumes in Practice Now let’s take a look at how Docker Volumes can be used in practice. Suppose we're developing an application to collect specific types of data — let’s say football statistics. We gather this data and plan to use it later for analysis — for example, to assess players’ transfer market values or for betting predictions. Let’s call our application FootballStats. Preserving Data After Container Removal Obviously, if we don’t use Docker Volumes, all the collected statistics will simply be lost as soon as the container that stored them is destroyed. Therefore, we need to store the data in volumes so it can be reused later. To do this, we use the familiar -v option:  -v footballstats:/dir/footballstats This will allow us to store match statistics in the /dir/footballstats directory, on top of all container layers. Sharing Data Suppose the FootballStats container has already gathered a certain amount of data, and now it's time to analyze it. For instance, we might want to find out how a particular team performed in the latest national championship or how a specific player did — goals, assists, cards, etc. To do this, we can mount our volume into a new container, which we’ll call FootballStats-Analytics. The key advantage of this setup is that the new container can read the data without interfering with the original FootballStats container’s ongoing data collection. At the same time, analysis of the incoming data can be performed using defined parameters and algorithms. This information can be stored anywhere, either in the existing volume or a new one, if needed. Other Types of Mounts In addition to standard volumes, Docker Volumes also supports other types of mounts designed to solve specialized tasks: Bind Mount Bind mounts are used to attach an existing path on the host to a container. This is useful for including configuration files, datasets, or static assets from websites. To specify directories for mounting into the container, use the --mount option with the syntax <host path>:<container path>. Tmpfs Mount Tmpfs mounts serve the opposite purpose of regular Docker Volumes — they do not persist data after the container is destroyed. This can be useful for developers who perform extensive logging. In such cases, continuously writing temporary data to disk can significantly degrade system performance. The --tmpfs option creates temporary in-memory directories, avoiding constant access to the file system. Drivers Docker Volume Drivers are a powerful tool that enable flexible volume management. They allow you to specify various storage options, the most important being the storage location — which can be local or remote, even outside the physical or virtual infrastructure of the provider. This ensures that data can survive not only the destruction of the container but even the shutdown of the host itself. Conclusion So, we’ve learned how to create and manage storage using Docker Volumes. For more information on how to modify container storage in Docker, refer to the platform’s official documentation. 

Every developer strives to make the product development process faster while maintaining flexibility and effective control. Microservices architecture makes this possible, and over the past 7–10 years, it has become a strong alternative to the traditional monolithic approach. Let’s begin by exploring the difference between the two. Microservices Architecture vs. Monolith The difference between these two approaches to software development is best illustrated with an example. Suppose we have two online stores: one built as a monolith, the other using microservices. A monolithic online store is a single, indivisible structure that combines all components: databases (product catalog, customer data), cart, order and payment forms. All elements are tightly coupled and reside on the same server. In a microservices-based system, each component is an independent module that developers can work on separately. Of course, these modules don't have to be hosted on the same server. Thus, microservices architecture is like a modular building kit where you can easily add new components and scale the application. A monolith, in contrast, is like a solid wall — and scaling it typically means duplicating the entire structure. It’s also worth noting that microservices are sometimes mistakenly thought of as just a collection of tiny services. That’s not true. For example, the database of a large e-commerce site may contain millions of records and take up tens of gigabytes, yet still be just one module within a microservices-based application. Comparing Microservices and Monoliths by Key Criteria Let’s compare how microservices and monoliths address the same development needs. Release Cycle Microservices allow for faster development and more frequent releases thanks to their modularity — updates affect individual modules rather than the whole codebase. With a monolith, you must update the entire platform, which increases testing time and delays releases. Technology Stack Microservices offer much greater flexibility: each service can use its own programming language, libraries, and data storage technologies. With a monolith, the technology stack is fixed and hard to change, forcing developers to stick with the original tools. Onboarding Developers Since each microservice is a standalone unit, developers can be onboarded to specific modules without needing to understand the entire system. In a monolith, new developers must familiarize themselves with the entire application codebase before contributing effectively, making the team more dependent on specific individuals. Optimization The modularity of microservices simplifies optimization, as each module can be tuned separately. In monoliths, optimization is more complex due to tight coupling — changes in one part often affect the entire system. Scalability Microservices, being distributed and potentially deployed on separate servers, make it easier and faster to scale specific components. In monoliths, scaling one part usually means scaling the entire application, which is inefficient. Fault Tolerance Thanks to their distributed and modular nature, microservices offer higher fault tolerance. A failure in one module does not affect the whole system. In a monolith, components are tightly connected, and a failure in one part can bring down the entire application. Should You Switch to Microservices Now? Microservices clearly offer several advantages. But does that mean monoliths are outdated and should be replaced immediately? Not necessarily — it depends on your current project status. In fact, switching to microservices isn't always the best move. Distributed systems also come with their own challenges: Network Dependency: Microservices require robust network communication. Unstable connections can cause delays or data inconsistencies, potentially disrupting the application. Increased Overhead: Each module must be separately tested and monitored. You’ll also need to allocate cloud resources for each, which can increase costs. Team Coordination: Microservices can introduce coordination challenges between teams managing different modules. This often requires DevOps specialists to bridge gaps between developers and streamline collaboration. Considering all these factors, the switch to microservices should be well-timed. In most early-stage projects, especially those with limited teams or budgets, there's no urgent need to move away from a monolith. You should consider transitioning to microservices when: You have a large team — it makes sense to split them into independent groups, each managing a specific service. Your application is complex and modular — maintaining and updating modules separately is more practical. Your application experiences traffic spikes — distributed microservices allow you to scale quickly during peak times and scale down afterward. Your application is frequently updated — working on individual modules speeds up release cycles. If your project meets even one of these criteria, it's worth exploring microservices. But if your app is relatively small and doesn’t need frequent updates, it might be best to stick with the monolithic approach for now. Useful Tools for Microservices Architecture Modern development requires a containerization platform. In most cases, developers use Docker to isolate applications from infrastructure, enabling them to run seamlessly both locally and in the cloud. As the number of containers grows, you need an orchestrator to manage them. The most popular tool is Kubernetes, which integrates well with Docker. Docker also has its own orchestrator: Docker Swarm. Another essential tool is a load balancer, which evenly distributes network traffic across cloud resources, significantly improving the application's fault tolerance.