Nextcloud vs Owncloud: What to Choose and How Much Does It Cost?
Infrastructure
26.10.2021
10 min read
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Let’s talk about Nextcloud and ownCloud. What are they supposed to do and why might your team want to use one. Here is a detailed breakdown of the most popular and functional progressive cloud services.
What are Nextcloud and ownCloud?
Basically, both applications are digital coworking services — online platforms for working together as a team on any project while having access to one filing system and additional software products.
The idea behind these products lies in cooperative working without limitations. Nextcloud and ownCloud both help teams to stay connected, edit the same files in parallel, and get the job done faster.
Additionally, it gives control of email to the entire team and not just one teammate. Edit rich-text documents, talk to each other using fully-fledged messengers and call-apps bundled with Nextcloud.
But the unique feature of these apps is the ability to create a private space and work closely with team members.
There are many digital products that make it possible for different people to edit the same files (Evernote, Google Docs, Dropbox, Office 365, etc.) but none of them respects your privacy. With powerful products made by Microsoft, Apple, Amazon, etc. you end up giving them a lot of your personal data. Since ownCloud and Nextcloud are open-source projects you stay in control. These services allow you to avoid corporations while at the same time gaining access to their software products. That’s why both ownCloud and Nextcloud are extremely popular.
What is Nextcloud used for
Nextcloud is a cloud service that includes many tools for working collaboratively. The main member of the Nextcloud family is Nextcloud Hub. This is software that harnesses every tool your team needs to communicate faster, work together and remain aware of any changes to the project.
Nextcloud Files
This is an online filing system that can be accessed by anyone from your team via a web browser or mobile application. It looks like and feels like Google Drive but it’s a private one.
Nextcloud Talk
This one resembles Zoom or Discord. It’s a fully functional platform to communicate with your teammates, share files, and set up phone conferences using mobile devices or a web interface.
Nextcloud Groupware
This is a system of multiple applications including a calendar to prepare a shared schedule, email clients from one email inbox, and enjoy full control of a shared contact list.
Other tools
There are also modules to connect FTP, SharePoint, and other types of servers, and the Nextcloud Flow interface helps to optimize repetitive tasks your team used to do.
How much does Nextcloud cost?
Nextcloud is an open-source project and you can use the distributive for free. But to do this, you’ll have to set it up yourself on your server without the help of specialists.
There’s also Nextcloud Enterprise — a derived project that aims to help large corporations and small businesses access all the tools that Nextcloud offers. It has three plans:
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Basic — this one includes a maintenance lifecycle of one year, faster tech support reaction time, fast deployment of security patches, and the opportunity to integrate the system with Outlook software. It costs 36 euros per user per year for teams of one hundred or fewer teammates and 28.50 euros per user for teams of two hundred or more teammates.
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Standard — this one includes all the features that you can find in the basic plan but also: branding (helps to reconfigure the whole system to be more consistent with corporate identity), additional optional components like Collabora Online Office, HANCOM Works, Nextcloud Groupware, and more. Costs 65 euros per year for small teams and 48.50 for large ones.
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Premium — gives you all the add-ons you might want to install including Microsoft Office Online. It also includes technical support which provides immediate help 24/7. This costs 95 euro per user per year for small teams or 74.50 euros per year for bigger ones.
But you can use hosts like Hostman that offer preinstalled Nextcloud with all the basic functions.
How to setup Nextcloud server
System requirements
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You should use one of the modern and up-to-date Linux distributives like Ubuntu 20.04, RHEL 8, Debian 10, CentOS 8, etc.
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On your server install MySQL, MariaDB, Oracle Database or PostgreSQL. NoSQL databases are not supported.
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The machine on which Nextcloud is deployed should run an Apache or nginx web server (the first one is recommended).
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It is better to have a modern version of PHP installed.
Installing Nextcloud on Ubuntu Linux
After preparing all the prerequisites listed in the previous part of the article you should visit the official site of the cloud service and download the Nextcloud Server package there.
You’ll have a zip archive that you must extract to the directory named "Nextcloud".
Configure Apache server
You need to create a configuration file /etc/apache2/sites-available/nextcloud.conf.
Inside this file put the following, changing the paths to ones that fit your server preferences:
Alias /nextcloud "/var/www/nextcloud/"
Require all granted
AllowOverride All
Options FollowSymLinks MultiViews
Dav offWhen the server is set up and running you should visit http://localhost/nextcloud and move forward by following the installer’s commands.
What is ownCloud used for?
ownCloud is quite different to Nextcloud. It is actually a platform which branched away from Nextcloud some time ago. The main project was launched first and was then used as a collaborative online tool much earlier than Nextcloud. This is why ownCloud is likewise considered a safe and functional way to store data and grant access to teammates.
ownCloud respects rules such as GDPR (General Data Protection Regulation), LGPD, CCFR (Cloud Computing Regulatory Framework), HIPAA and CCPA. These regulations are designed to protect your data from misuse.
As for ownCloud features, developers highlight the following:
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Fully functional mobile applications that are interchangeable with their desktop counterparts.
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Automation support on iOS and new macOS versions.
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Advanced state-of-the-art files encryption system.
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Communication mechanisms to stay in touch with your team.
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Document scanner integrated into iOS for iPhone and iPad.
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Ability to search through the text inside files stored in ownCloud.
How much does ownCloud cost?
There are 4 different plans for ownCloud. 2 for self-hosted servers and 2 for their proprietary online services.
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Standard — gives access to the customer portal, lets you use mobile apps, activate sync, and share files. Costs 5 euro per year per user. The team should comprise at least 25 teammates.
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Enterprise — extended plan that includes Enterprise functions. Costs 12 euros annually per user. The team should consist of at least 25 teammates.
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For Teams — server hosted on ownCloud’s site in Germany. Comes with 1000 GB of cloud storage, 200 GB for every new user, 180 days of data recovery, multifactor authentication, firewall and other useful features. Costs 13 euro per user. Paid annually.
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For Single Users — same as "For Teams" but for smaller groups of users.
How to setup ownCloud server?
System requirements
For best performance developers of ownCloud recommend using:
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Ubuntu 20.04 LTS
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MariaDB 10+
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Apache 2.4 with prefork and mod_php
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PHP 7.4
Installing ownCloud on Ubuntu Linux
You have to create helper script with these commands inside:
FILE="/usr/local/bin/occ"
/bin/cat <$FILE
#!/bin/bash
cd /var/www/owncloud
sudo -E -u www-data /usr/bin/php /var/www/owncloud/occ "\$@"
EOMAfter that you may download the official ownCloud distributive from its website and install it by using command:
occ maintenance:install \
--database "mysql" \
--database-name "owncloud" \
--database-user "owncloud" \
--database-pass "password" \
--admin-user "admin" \
--admin-pass "admin"Configure Apache server
You should set up a server and Virtual Host Configuration. Then you must enable created configuration and change database preferences to correspond with parameters of other server components. Instructions for doing this correctly can be found within ownCloud’s documentation articles.
Nextcloud and ownCloud on Windows and any other platform
Unfortunately, there’s no way to install Nextcloud or ownCloud as a server on any platform besides Linux or FreeBSD. That means that the core of these software products must be deployed on Unix-based OS (macOS is also Unix-based but can’t serve as a server for Nextcloud or ownCloud either). If you want to set up a server on Windows or macOS you should consider alternative cloud services or use virtual machines.
You might know that a lot of developers that use Windows as the main operating system actually develop in Linux environments using Windows Subsystems for Linux. It is a kind of virtual PC inside your PC that runs proper Linux distribution. And since it is a real Linux OS you can deploy Nextcloud there as you would do with Ubuntu. Just visit the Windows Store and find the last version of Ubuntu there. Or download applications like VirtualBox or VMWare.
But if you do not want to deploy Nextcloud or ownCloud but get an app to connect to an already functioning cloud service’s instance you might want to head to the official website of the service and download the client there. Both applications offer clients for Windows, Linux, macOS, iOS, and Android. The installation process depends on the chosen platform and is usually not so different from installing any other app.
Nextcloud and ownCloud on Raspberry Pi
In order to install one of the cloud services as a server on your mini-computer, it should be running Linux or FreeBSD. The process of installation is not really different from installing the same software on any Unix-based machine that supports either Nextcloud or ownCloud.
What is better: Nextcloud or ownCloud?
Nextcloud is a good all-in-one solution for most users. It is great for those teams that want to access a lot of useful tools without setting up too many things.
Of course, it is great at working with files. Sharing, coediting, version controlling, etc. But Nextcloud is much more than a remote filing system.
Nextcloud Enterprise helps to deploy a full-fledged cooperative workspace with incredible software components like Nextcloud Talk and Nextcloud Groupware. So your team doesn’t need to use third-party applications to address any challenges that appear before them. At any rate, it is more powerful software that your team can get on much better terms.
In contrast to Nextcloud, ownCloud is focused on working with files only. There are many tools that help teams around the world to handle their documents, photos, presentations, and arts seamlessly. That’s why you won’t find services like Nexcloud Hub here. ownCloud is all about small features created to improve your file sharing and collaborative editing capabilities.
But at the same time, it is an open-source platform with a distinguished API that can be used to create powerful plugins broadly extending the application’s feature set. It means that you can pretty much copy most of the Nextcloud features to ownCloud, and they will be aligned.
Also, it might brag about much faster tech support (which responds within two hours when Nextcloud’s one may make you wait for 2 days), freely available documentation, community edition Windows Desktop Client, Storage certification, etc.
The most lucrative and simple way to deploy Nextcloud
We already mentioned Hostman as a good host to deploy Nextcloud, so let’s get a bit deeper.
Hostman has a marketplace — an online shop with a series of one-click-deploy services. You can find their Minecraft gaming server, different databases, analytics tools, and Nextcloud of course.
The simplest way to start working with this cloud service is to visit its official page in Hostsman’s marketplace and click on the "Launch Nextcloud now" button.
It will create a server with Nextcloud preinstalled and set up. You won’t need to bother about the installation procedure and prerequisites. Everything will be ready for basic configuration and launching.
This service costs 19 dollars per month and if you want to try it our first Hostman offers a 7 day free trial without any restrictions.
Summary
As you see, both Nextcloud and ownCloud are functional and useful instruments to set up cooperative workspaces online. Moreover, you now know what tool to choose and how to make the whole process incomparably beneficial for your team. Don’t forget about Nextcloud system requirements and the security of shared files.
Infrastructure
26.10.2021
10 min read
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Infrastructure
Data Processing Unit (DPU): Meaning & How It Works
A DPU is a special type of processor designed for data processing. The abbreviation stands for Data Processing Unit. Technologically, it is a kind of smart network interface card.
Its main purpose is to offload the central processing unit (CPU) by taking over part of its workload.
To understand why DPUs are important and what potential this technology holds, we need to go back several decades.
A Bit of History
In the 1990s, the Intel x86 processor, combined with software, provided companies with unprecedented computing power. Client-server computing began to develop, followed by multi-tier architectures and then distributed computing. Organizations deployed application servers, databases, and specialized software, all running on numerous x86 servers.
In the early 2000s, hypervisors became widespread. Now, multiple virtual machines could be launched on a single powerful server. Hardware resources were no longer wasted and began to be used efficiently.
Thanks to hypervisors, hardware became programmable. Administrators could now write code to automatically detect and initiate virtual machines, forming the foundation of today’s cloud computing paradigm.
The next step was network and storage virtualization. As a result, a powerful CPU became the foundation for emulating virtually everything: virtual processors, network cards, and storage interfaces.
The downside of this evolution was that pressure on the CPU increased significantly. It became responsible for everything, from running the operating system and applications to managing network traffic, storage I/O operations, security, and more. All system components began competing for CPU resources.
The CPU’s functions went far beyond its original purpose. At this point, two major trends emerged:
The appearance of specialized hardware for artificial intelligence (AI).
The evolution of programmable hardware.
CPU, GPU, and DPU
AI workloads require parallelism, which cannot be achieved with a general-purpose CPU. Thus, graphics processing units (GPUs) became the driving force behind AI development.
Originally designed to accelerate graphics rendering, GPUs evolved into coprocessors for executing complex mathematical operations in parallel. NVIDIA quickly seized this opportunity and released GPUs specifically designed for AI training and inference workloads.
GPUs were the first step toward offloading the CPU. They took over mathematical computations. After that, the market saw the emergence of other programmable chips.
These microchips are known as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs), which can be programmed for specific tasks, such as optimizing network traffic or accelerating storage I/O operations. Companies like Broadcom, Intel, and NVIDIA began producing processors that were installed on network cards and other devices.
Thanks to GPUs and programmable controllers, the excessive load on the CPU started to decrease. Network functions, storage, and data processing were delegated to specialized hardware. That’s the simplest explanation of what a coprocessor is: a device that shares the CPU’s workload, allowing hardware resources to be used to their full potential. The secret to success is simple: each component does what it does best.
Understanding the Architecture
Before discussing DPUs, we should first understand what an ASIC processor is and how it relates to network interface cards.
Standard and Specialized Network Cards
A network card is a device that allows a computer to communicate with other devices on a network. They are also referred to by the abbreviation NIC (Network Interface Controller).
At the core of every NIC is an ASIC designed to perform Ethernet controller functions. However, these microchips can also be assigned other roles. The key point is that a standard NIC’s functionality cannot be changed after manufacturing; it performs only the tasks it was designed for.
In contrast, SmartNICs have no such limitations. They allow users to upload additional software, making it possible to expand or modify the functionality of the ASIC, without even needing to know how the processor itself is structured.
To enable such flexibility, SmartNICs include enhanced computing power and extra memory. These resources can be added in different ways: by integrating multi-core ARM processors, specialized network processors, or FPGAs.
DPU Characteristics
Data Processing Units are an extension of SmartNICs. Network cards are enhanced with support for NVMe or NVMe over Fabrics (NVMe-oF).
A device equipped with an ARM NVMe processor can easily handle input/output operations, offloading the central processor. It’s a simple yet elegant solution that frees up valuable CPU resources.
A DPU includes programmable interfaces for both networking and storage. Thanks to this, applications and workloads can access more of the CPU’s performance, which is no longer burdened with routine network and data management tasks.
Market Solutions
One of the best-known solutions is NVIDIA® BlueField, a DPU line first introduced in 2019, with the third generation announced in 2021.
NVIDIA BlueField DPU is designed to create secure, high-speed infrastructure capable of supporting workloads in any environment. Its main advantages include:
Zero-trust architecture, ensuring strong security within data centers.
Low latency with direct data access.
Data transfer speeds up to 400 Gbit/s.
SDKs that help developers build high-performance, software-defined, cloud-optimized services accelerated by DPUs using standard APIs.
Another company in this space is Pensando, which develops the Distributed Services Card, a data-processing card featuring a DPU. It includes additional ARM cores and hardware accelerators for specific tasks such as encryption and disk I/O processing.
Google and Amazon are also developing their own ASIC-based projects:
Google TPU (Tensor Processing Unit): a processor designed for machine learning, optimized to run advanced ML models in Google Cloud AI services.
AWS Graviton: an ARM-based chip designed to provide the best performance-to-cost ratio for cloud workloads running in Amazon EC2.
What’s Next?
It is quite possible that the DPU will become the third essential component of future data center servers, alongside the CPU (central processing unit) and GPU (graphics processing unit). This is due to its ability to handle networking and storage tasks.
The architecture may look like this:
CPU: used for general-purpose computing.
GPU: used for accelerating AI applications.
DPU: used for processing and transferring data.
It appears that DPUs have a promising future, largely driven by the ever-growing volume of data. Coprocessors can breathe new life into existing servers by reducing CPU load and taking over routine operations. This eliminates the need to look for other optimization methods (such as tweaking NVIDIA RAID functions) to boost performance.
Estimates suggest that currently, around 30% of CPU workload is consumed by networking functions. Transferring these tasks to a DPU provides additional computing power to the CPU. This can also extend the lifespan of servers by several months or even years, depending on how much CPU capacity was previously dedicated to networking.
By adding a DPU to servers, clients can ensure that CPUs are fully utilized for application workloads, rather than being bogged down by routine network and storage access operations. And this looks like a logical continuation of the process that began over 30 years ago, when organizations started building high-performance systems based on a single central processor.
09 October 2025 · 6 min to read
Infrastructure
Service Level Agreement (SLA): Meaning, Metrics, Examples
An SLA is an agreement that defines the level of service a company provides to its customers. This term is usually used in IT and telecommunications.
Unlike standard service contracts, a Service Level Agreement provides a very detailed description of service quality, operating modes, response times to incidents, and other parameters.
Main Characteristics of an SLA
A Service Level Agreement usually has the following characteristics:
Maximum possible transparency of all processes and interactions between the service provider and the client. When drafting the contract, vague wording that could be interpreted ambiguously in one direction or another is avoided.
Clearly defined rights and obligations understood by all participants in the agreement. For example, a provider commits to ensuring 99.9% service availability and to pay compensation if a lower figure is recorded, while the client has the right to request that compensation.
Expectation management. For instance, a client might expect 24/7, ultra-fast support even for minor issues, while the provider cannot offer such a service. In this case, the client should either lower their expectations or sign a contract with another provider. A third option is also possible: the provider may raise the service level if it benefits their business processes.
The agreement specifies the timeframes for fixing issues and solving other problems. It also describes possible compensations that the client may receive if the company fails to meet the declared metrics.
An SLA does not always need to be a large document. The main thing is that it clearly describes the core parameters of the service in understandable terms. For example, the AWS S3 SLA is only one page long. It lists monthly uptime percentages and the amount of compensation the client receives if the service fails to meet those thresholds.
What is Usually Included in an SLA
The example above from Amazon Web Services is not a standard; it is just one possible format tailored to a specific service.
An IT SLA often includes the following sections:
The procedure for using the service.
Responsibilities of both parties, including tools for mutual monitoring of performance.
Specific steps for troubleshooting and restoring functionality.
The agreement may also specify its term. In some cases, the parties describe in detail the procedure for adding new requirements for functionality or service availability.
When describing service quality, its parameters are also disclosed. These typically include:
Service availability.
Response time to a problem.
Time to fix incidents.
The SLA may also specify a metric for operating hours.
When describing payment procedures, it may indicate the billing model (e.g., pay-as-you-go, fixed rate, etc.). If penalties are provided, the SLA will specify the situations in which the provider must pay them. If the client is entitled to compensation, the SLA also describes the relevant situations and payment procedures.
Key SLA Parameters
SLA parameters are metrics that can be measured. The agreement should not contain vague phrases like “issues will be resolved quickly, before you even notice.” Such wording is unclear and prevents all participants from organizing proper workflows.
For example, the support schedule metric should clearly define when and for which groups of users technical support is available.
Suppose a company divides its clients into several groups:
Group 1: 24/7 phone and chat support.
Group 2: phone and chat support only on weekdays.
Group 3: chat-only support on weekdays.
Metrics are necessary so that all participants understand which services they receive, when, and in what scope.
From this, several key characteristics follow:
Metrics must always be publicly available.
Their descriptions must be unambiguous for all parties.
Clients must be notified in advance about metric changes.
When defining metrics, it’s important not to set overly strict requirements, as this significantly increases costs.
For instance, suppose a typical specialist can resolve a problem in 4 hours, while a higher-level expert can do it in 2 hours. Writing “2 hours” as the SLA metric is not ideal, as it would immediately make the expert’s work more expensive. If you specify “1 hour,” costs rise further due to the increased risk of penalties for non-compliance.
Other important metrics can include response time to a client request. The values may differ depending on the client’s status and problem criticality.
For example, a company providing IT outsourcing services might have:
Premium clients: response within 15 minutes.
Basic clients: response within 24 hours.
All of this must be clearly reflected in the SLA.
In addition to response time, there’s also incident resolution time. The logic for this parameter is similar: even if a client is important, requests are prioritized based on criticality.
For example:
If a client’s local office network stops working and all processes halt, that issue must be prioritized. The SLA may state that local network troubleshooting should take no more than 5 hours.
If the same client needs to add a few new devices to an already working network, the resolution time may be several hours or even days.
The combination of response time and resolution time forms downtime.
These and other parameters must be described in the SLA and accepted by all parties before cooperation begins. This approach reduces conflicts; everyone understands what to expect from each other.
Service Availability
For providers, one of the most important SLA parameters is service availability. It is usually measured in days, hours, or minutes over an agreed period. For example, a provider guarantees that a cloud computing service will be available 99.99% of the time during a year.
At first glance, the difference between SLA 99 and SLA 100 may seem small. But in absolute terms, it’s significant.
At 99%, you agree that servers may be down up to 4 days per year.
At 100%, downtime should be zero—something no company can guarantee.
That’s why SLAs are usually written with “nines”: e.g., 99.9%, 99.99%, etc.
For example, Hostman.com guarantees 99.98% uptime, meaning total annual downtime will not exceed 1 hour 45 minutes.
Some providers promise “five nines”: 99.999% uptime, or less than 15 minutes of downtime per year. But this is not always the best option. Two points to consider:
The higher the SLA percentage, the higher the cost.
Not every client needs such a high level. In most cases, 99.982% uptime (or slightly higher) is sufficient.
It’s important to check not only the number of nines but also the time unit used for measurement. By default, SLA indicators are calculated annually. For example, 99.95% availability equals no more than 4.5 hours of downtime per year.
If the contract doesn’t explicitly say that the time unit is “per year,” be sure to clarify, as some providers disguise monthly values as annual.
Another key concept is aggregate availability, which equals the lowest of all measured values.
Benefits of an SLA
Signing and adhering to an SLA benefits both parties.
For the company, it defines obligations and protects against unreasonable client demands, such as urgently fixing a minor issue in the middle of the night.
Other benefits include:
The provider can use the SLA to organize both external and internal processes, such as introducing different support levels depending on service criticality and client importance.
Clients gain clarity about what services they can expect, in what timeframes, and in what order, helping them plan their core operations.
SLA vs. SLO: What’s the Difference
An SLA can also be viewed as an indicator of user satisfaction, ranging from 0% to 100%.
Absolute satisfaction (100%) is impossible, just as it’s impossible to guarantee 100% uptime. Therefore, when choosing metrics, one should be realistic and select achievable values.
For example, if your team doesn’t provide 24/7 support, you shouldn’t promise it. When the team expands, you can update the SLA and delight clients by offering round-the-clock assistance.
To monitor service levels internally, another system is used: SLO (Service Level Objective). These are the target values the provider aims to achieve.
Example: Current capabilities are handling 50 tickets per business day, working 9:00 to 18:00, five days a week. These metrics are fixed in the SLA and shown to clients.
Meanwhile, the SLO document sets internal goals, for example, increasing the number of handled tickets to 75 per day or switching to 24/7 support. This directly affects the company’s future service level.
How to Create a Proper SLA
Start with a descriptive section, which usually includes:
A glossary
System description
Participant roles (users, support specialists)
Boundaries of operation: geography, time, functionality
The next section describes the services provided, giving the client a full understanding of what they can expect when signing with the provider.
Then comes the main section, describing the service level. It should include metrics that reflect quality and are easily measurable, as well as metric values that are specific numbers guiding all participants.
You can end the SLA with references to other documents that regulate service processes.
At all stages of preparing an SLA, remember: it is a regulatory document. Its main goal is control. The more control over the process, the better the SLA. If there is no control, such an agreement is meaningless.
Checklist: What to Consider When Preparing an SLA
If you are not signing but drafting an SLA to offer clients, pay attention to the following points:
Users. In large systems, divide users into groups and manage them separately. This helps allocate resources efficiently and avoid overload from different client types.
Services. Consider the criticality of each service for each client group. Example: You provide a CRM to trading companies. If they can’t use it, they lose money and complain, meaning it’s a high-criticality service. Printer replacement or user account creation can wait until tomorrow.
Service quality parameters. They must align with business goals and client needs. A typical example is incident resolution times, e.g., 24/7 support versus 9 a.m. to 5 p.m. on weekdays only.
An SLA is a document that must be announced to all users whenever it is introduced or updated, regardless of privilege level or service criticality.
SLA is a management tool that constantly evolves. You may find that current quality parameters harm business processes or no longer meet client expectations. In that case, management should decide to optimize processes or improve services.
The main goal of SLA indicators is not to attract users but to ensure open dialogue with them. Every participant accepts the agreement and commits to following it.
Violation of an SLA is grounds to claim compensation and terminate cooperation.
09 October 2025 · 9 min to read
Infrastructure
What is Docker: Application Containerization Explained
Docker is software for containerizing applications. Today, we’ll talk about what containerization and Docker are, what they are used for, and what advantages they bring.
Containerization
Containerization is one of the methods of virtualization. To understand it better, let’s take a brief historical detour.
In the 1960s, computers couldn’t perform multiple tasks at once. This led to long queues for access to such rare machines. The solution was to distribute computing power among different isolated processes. That’s how the history of virtualization began.
Virtualization is the allocation of computing resources to isolated processes within a single physical device.
The main development of virtualization came during the Internet era. Imagine you’re a business owner and you want your company to have a website. You need a server connected to the global network. Today, that’s as easy as visiting hostman.com and choosing a server that fits your needs.
But in the early days of the internet, such convenient services didn’t exist. Companies had to buy and maintain servers on their own, which was inconvenient and expensive. This problem led to the rise of hosting providers: companies that purchased hardware, placed it in their facilities, and rented out servers.
As technology advanced, computers became more powerful, and dedicating a full physical server to a single website became wasteful. Virtualization helped: several isolated virtual machines could run on one computer, each hosting different websites. The technology allowed allocating exactly as many resources as each site needed.
However, that still wasn’t enough. As the internet evolved, the number of applications required for running a website grew, and each required its own dependencies. Eventually, it became “crowded” within a single virtual machine. One workaround was to host each application in its own virtual machine, a kind of virtual “matryoshka doll.” But a full VM was still excessive for a single application: it didn’t need a full OS instance. Meanwhile, virtual machines consumed a lot of resources, much of which went unused.
The solution was containerization. Instead of running a separate virtual machine for each application, developers found a way to run them in isolation within the same operating system. Each container includes the application, its dependencies, and libraries: an isolated environment that ensures consistent operation across systems.
Docker
What is a program? It’s a piece of code that must be executed by the CPU.
When you run a container, Docker (through the containerd component) creates an isolated process with its own namespace and file system. To the host system, the container looks like a regular process, while to the program inside it, everything appears as if it’s running on its own dedicated system.
Containers are isolated but can communicate with each other via networks, shared volumes, or sockets, if allowed by configuration.
Data Storage
Isolation from the host OS raises a natural question: how to store data?
Docker Volume: a storage unit created and managed by Docker itself. It can be located anywhere: within the host’s file system or on an external server.
Bind Mount: storage manually created by the user on the host machine, which is then mounted into containers during runtime.
tmpfs Volume: temporary in-memory storage. It is erased when the container stops.
In production environments, volumes are most commonly used, as Docker manages them more securely and reliably.
Docker Architecture
Docker’s architecture consists of several key components that work together to build, run, and manage containers:
Docker Host
A physical or virtual machine running the Docker Engine. This is where containers and images are executed.
Docker Engine (Docker Daemon)
The central service responsible for building, running, and managing containers. Since Docker 1.11, Docker Engine has used containerd, a low-level component that directly manages container lifecycles (creation, start, stop, and deletion).
containerd
A container runtime that interacts with the operating system kernel to execute containers. It’s used not only by Docker but also by other systems such as Kubernetes. Docker Engine communicates with containerd via an API, passing commands received from the client.
Docker CLI (Client)
The command-line interface through which users interact with Docker. CLI commands are sent to the Docker Daemon via REST API (usually over a Unix socket or TCP).
Docker Image
A Docker image is a template that includes an application and all its dependencies. It’s similar to a system snapshot from which containers are created.
Dockerfile
A text file containing instructions on how to build an image. It defines the base image, dependency installation commands, environment variables, and the application’s entry point.
Docker Container
A Docker container is a running instance of an image. A container is isolated from other processes and uses host resources through Docker Engine and containerd.
Docker Registry
A repository for storing and distributing Docker images. There are public and private registries. The most popular public one is Docker Hub, which Docker connects to by default.
Docker Compose
A tool for defining and running multi-container applications using YAML files. It allows developers to configure service dependencies, networks, and volumes for entire projects.
Advantages of Docker
Security
What does isolation provide in terms of security?
An isolated application cannot harm the host operating system.
It has no access to the host’s file system, preventing data leaks.
Any application-related crash won’t affect the host OS.
Compatibility
A container image can be run on any device with Docker installed.
Automation
Docker automates application deployment and configuration, saving time and reducing human error.
Shared Repositories
Docker users have access to repositories with thousands of ready-to-use images for various purposes.
Resource Efficiency
Unlike virtual machines, Docker containers don’t require a separate OS instance, allowing better use of computational resources.
Using Docker
Now let’s move from theory to practice. The first thing we need to do is install Docker.
Installation
Installation begins at the official website: docker.com. Go to the “Get Started” section and choose the version for your operating system. In our case, it’s Windows. Installation guides for other OSs are also available. After installation, a system reboot is required.
Docker requires a hypervisor, special software that enables multiple operating systems to run simultaneously. We’ll use WSL2 (Windows Subsystem for Linux 2).
Docker installs WSL2 automatically, but you must manually download the latest Linux kernel update. Go to Microsoft’s website, download, and install the update package. After rebooting, Docker Desktop will open.
Running a Python Script
Let’s print the message “Hello, World” to the console using a simple Python script:
#!/usr/bin/python3
print("Hello World")
Since we’re not running the script directly, we need a shebang—that’s the first line in the script. In short, the shebang tells the Linux kernel how to execute the script. Let’s name our file the classic way: main.py.
Now open the command line. To run the script, execute:
docker run -v D:\script_dir:/dir python:3 /dir/main.py
Let’s break this down:
docker run runs a container
-v mounts a directory (bind mount)
D:\script_dir is the directory with our script
/dir is the mount point inside the container
python:3 is the image
/dir/main.py is the executable file (our script)
What happens when this command is executed? Docker searches for the python:3 image first locally, then in the registry, and deploys it. Next, it mounts our script directory into the container and runs the script inside it.
Conclusion
In this article, we explored what Docker is, how it works, and even ran our first script. Docker and containerization are not a cure-all, but they’re invaluable tools in modern software development.
08 October 2025 · 7 min to read
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