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7 Data Analytics Trends for 2022 That You Need to Watch Out For
Infrastructure
27.05.2022
9 min read
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Over the past few years, data analytics has become a goldmine. The more data a company has, the more insight they have to make smarter decisions. With the data industry worth an estimated $274 billion in 2022, it’s no wonder big data is the driving force for the future.
The pandemic continues to drive business digital transformations. Applications have an improved capacity to interpret incoming data for its user. This means that the opportunities for business growth are endless.
Organizations embrace the cutting-edge data technology available to them. Data analytics continues to evolve, from big data to artificial intelligence (AI), data-driven models are in demand more than ever.
With the pandemic and its economic disruptions, businesses now realize they need to better use the data available to them. With data analytics traditionally used to assess ‘what happened?’ It is now used to predict ‘what will happen?’
With that in mind, let’s take a look at the top seven data analytics trends for 2022 and how they can benefit your business.
Smarter artificial intelligence
With the introduction of artificial intelligence (AI), organizations are experiencing a specialized change in business strategy. AI accelerates business decision making by automating processes that determine data analytics.
By developing training models and testing metrics in agile methodology, AI ensures rapid data insight without the need for data scientists. Organizations are utilizing AI algorithms to measure, predict, and interpret large amounts of data. Data refers to the market, customers, and online applications.
The pandemic and the rise of remote work have increased opportunities to track and measure data. Due to this rise in data availability, establishing a new data-driven culture was necessary. A culture that fuels investments via AI-based technologies produces intuitive data analytics.
AI systems work with both large and small data sets. The systems protect privacy, are adaptive, and provide a faster return on investment. The advancement of intuitive UIs in services such as metabase analytical services promotes accessibility and efficiency. Simply put, it’s never been easier to interpret data.
Edge computing
CTO of Kinetica, Nima Negahban, describes edge computing as ‘data analysis that takes place on a device in real-time.’ She goes on to say that ‘edge computing is about processing data locally, and cloud computing is about processing data in a data center or public cloud.’
Edge computing delivers data analytics technology closer to the physical asset. From wrist wearables to mobile traffic apps, worldwide spending on edge computing is expected to reach $176 billion in 2022.
To introduce edge computing into business systems, industries need to include IoT app development and other data transformation services.
By processing data storage closer to the devices that collect it, edge computing is more reliable. With real-time processing there is no issue of latency, it is also more cost-efficient than cloud-based storage.
Cloud-based data solutions
As data is being produced in large quantities, there will be an increasing shift towards cloud-based solutions. A database in the cloud involves labeling, cleaning, formatting, and collecting. It equates to a mammoth amount of storage for one location. This is where cloud-based platforms come into play.
The cloud opens the door for the next generation of data warehousing. With added accuracy and security in the form of an existing QA framework. New practices such as data mesh, data fabric, and Data Vault 2.0 have been intrinsically built via the cloud.
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Data mesh - a holistic approach that allows data products connectivity across many domains. Enabling an information exchange without the need for storage.
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Data fabric - architecture that enables data access in a distributed environment.
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Data Vault 2.0 - based on the potential of the cloud, Data Vault 2.0 provides greater productivity. Driven by metadata for collaborative configuration and management of testing models.
Whilst a private cloud for your business can be costly, a hybrid cloud provides both private and public agility. A hybrid cloud approach offers companies the opportunity to switch between multiple cloud platforms. This is a more cost-effective approach for your business.
Data fabric
Due to our booming digital age, our interconnected ecosystem has become more complex than ever. Finding a solution to connect devices, applications, and data infrastructure formats is a constant challenge. Data fabric emerges as the solution.
Data fabric is a new answer to an old dilemma. With 73% of analytical data going unused, it has become essential for missing data to become discoverable. Data fabrics fuse data from internal silos and external data sources. This leads to the discovery of effective networks and business applications.
More companies are using data fabric architecture to produce more discoverable, pervasive, and reusable data from all environments. This includes private, public, and mulitcloud cloud systems.
Data fabric connects data from contrasting applications to identify new data relationships. It’s a form of referral software that enables rapid decision-making and cost-efficiency. Accelerating hybrid data integration ensures digital security and greater business value.
Data fabric is a technology that will become commonplace over the next few years.
Augmented analytics
Augmented analytics is a leading analytics concept that uses natural language processing (NLP), machine learning (ML), and AI. What used to be handled by a data scientist is now automated to offer economical data sharing and insight discovery.
Augmented analytics produces data integration from internal and external enterprises. Due to the specialist applications, the outcomes are more precise. NLP, ML, and AI ensure in-depth reports and forecasts, data processing, analytics, and visualization.
- Natural language processing - NLP provides computers with the ability to understand text and spoken words. It’s included in identifying patterns and trends in corporate operations. NLP is essential for tracking Twitter Analytics, understanding customer satisfaction, and smart assistants.
Machine learning - there is a growing use of data analytics and ML to predict what will happen in digital analytics and the best response to counter. Using ML to target specific customer needs, such as analyzing social media activity to determine what product they might buy.
- AI - AI helps make predictions more accurate, efficient, and cost-effective without the need for human intervention. AI will continue to be implemented in many different industries due to its unique possibilities.
Machine learning - there is a growing use of data analytics and ML to predict what will happen in digital analytics and the best response to counter. Using ML to target specific customer needs, such as analyzing social media activity to determine what product they might buy.With the introduction of all three, it has never been easier to measure, interpret, and predict results. Targeting specific customer behaviors reduces problems such as customers leaving their digital shopping cart abandoned and inefficient resources.
NLP, MI, and AI have the capabilities to assist business intelligence and aid business users.
Businesses are focussing on operational agility and resilience to recover from difficult market situations.
Automated machine learning
Applying ML models to real-world situations is known as automated machine learning (AutoML). ML is more user friendly when it is automated. It can create and deploy systems that even non-experts can master.
AutoML enhances monotonous workloads. Leaving human interaction free for more complemented questions (think of automation in call centers).
Businesses are counting on AutoML to increase data insight as end-users have direct access to these applications. As more users utilize this model, generating insight becomes easier and easier.
With the continuous release of machine learning tools, technical aspects of data science will become automated. This accelerates decision making by automating processes that data scientists would generally perform.
Much like how a mobile application is a must for small business development, AutoML is essential for rapid data insight. The more platforms that use it, the faster it becomes.
XOps
XOps has become an important fixture in business enterprise. It encompasses DataOps, ModelOps, AIOps, and PlatformOps, enabling automation of technology and processes. Making it a dominant combination of IT disciplines and strategic decision making when it comes to AI and machine learning (ML).
XOps data professionals can process defined goals that align with their business priorities. Its main focus is to enhance business operations as well as customer experience. Enhancing operations means enhanced security, which awards applications and offers advanced protection from DDoS attacks.
XOps strives to reduce duplication, ensuring more efficient and reliable data outcomes. Enabling XOps data and analytics allows the user to begin automation from the beginning rather than as an afterthought. It allows you to orchestrate your automating software in a way that meets measurable goals. Allowing more efficient data collection.
To cut a long story short
As data science continues to take the spotlight. Breakthroughs in future analytics continue to progress. Presently, data has never been more accessible, with companies able to collect, manage, analyze, and leverage data for future business intelligence.
Data analytics has become an essential part of business functionality. AI trends and data analysis provide valuable insight that improves business automation, accessibility, and intuition.
Organizations that successfully impose the above trends will be able to harness data strategically and efficiently. Tools such as automated machine learning and edge computing can improve customer algorithms via results and feedback.
Ensuring the process of data accumulation and evaluation is accessible, business actions have never been more necessary. Constantly improving analytics means businesses are constantly preparing for the future.
Author: Emily Rollwitz - Content Marketing Executive, Global App Testing
Emily Rollwitz is a Content Marketing Executive at Global App Testing, a remote and on-demand API automation testing tools company helping top app teams deliver high-quality software, anywhere in the world. She has 5 years of experience as a marketer, spearheading lead generation campaigns and events that propel top-notch brand performance. Handling marketing of various brands, Emily has also developed a great pulse in creating fresh and engaging content. She’s written for great websites like Airdroid and Shift4Shop. You can find her on LinkedIn.
Infrastructure
27.05.2022
9 min read
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Infrastructure
Virtualization vs Containerization: What They Are and When to Use Each
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.
10 June 2025 · 7 min to read
Infrastructure
Top RDP Clients for Linux in 2025: Remote Access Tools for Every Use Case
RDP (Remote Desktop Protocol) is a proprietary protocol for accessing a remote desktop. All modern Windows operating systems have it by default. However, a Linux system with a graphical interface and the xrdp package installed can also act as a server. This article focuses on Linux RDP clients and the basic principles of how the protocol works.
Remote Desktop Protocol
RDP operates at the application layer of the OSI model and is based on the Transport Layer Protocol (TCP). Its operation follows this process:
A connection is established using TCP at the transport layer.
An RDP session is initialized.
The RDP client authenticates, and data transmission parameters are negotiated.
A remote session is launched: the RDP client takes control of the server.
The server is the computer being remotely accessed.
The RDP client is the application on the computer used to initiate the connection.
During the session, all computational tasks are handled by the server. The RDP client receives the graphical interface of the server's OS, which is controlled using input devices. The graphical interface may be transmitted as a full graphical copy or as graphical primitives (rectangles, circles, text, etc.) to save bandwidth. By default, RDP uses port 3389, but this can be changed if necessary.
A typical use case is managing a Windows remote desktop from a Linux system. From anywhere in the world, you can connect to it via the internet and work without worrying about the performance of the RDP client.
Originally, RDP was introduced in Windows NT 4.0. It comes preinstalled in all modern versions of Windows. However, implementing a Linux remote desktop solution requires special software.
RDP Security
Two methods are used to ensure the security of an RDP session: internal and external.
Standard RDP Security: This is an internal security subsystem. The server generates RSA keys and a public key certificate. When connecting, the RDP client receives these. If confirmed, authentication takes place.
Enhanced RDP Security: This uses external tools to secure the session, such as TLS encryption.
Advantages of RDP
RDP is network-friendly: it can work over NAT, TCP, or UDP, supports port forwarding, and is resilient to connection drops.
Requires only 300–500 Kbps bandwidth.
A powerful server can run demanding apps even on weak RDP clients.
Supports Linux RDP connections to Windows.
Disadvantages of RDP
Applications sensitive to latency, like games or video streaming, may not perform well.
Requires a stable server.
File and document transfer between the client and server may be complicated due to internet speed limitations.
Configuring an RDP Server on Windows
The most common RDP use case is connecting to a Windows server from another system, such as a Linux client.
To enable remote access, the target system must be configured correctly. The setup is fairly simple and works "out of the box" on most modern Windows editions.
Enable remote desktop access via the Remote Access tab in System Properties.
Select the users who can connect (by default, only administrators).
Check firewall settings. Some profiles like “Public” or “Private” may block RDP by default. If the server is not in a domain, RDP might not work until you allow it manually via Windows Firewall → Allowed Apps.
If behind a router, you might need to configure port forwarding via the router’s web interface (typically under Port Forwarding). Recall that RDP uses TCP port 3389 by default.
Best RDP Clients for Linux
Remmina
Website: remmina.org
Remmina is a remote desktop client with a graphical interface, written in GTK+ and licensed under GPL. In addition to RDP, it supports VNC, NX, XDMCP, SPICE, X2Go, and SSH.
One of its key features is extensibility via plugins. By default, RDP is not available until you install the freerdp plugin. After installing the plugin, restart Remmina, and RDP will appear in the menu.
To connect:
Add a new connection.
Fill in connection settings (you only need the remote machine's username and IP).
Customize further if needed (bandwidth, background, hotkeys, themes, etc.).
Save the connection — now you can connect with two clicks from the main menu.
If you need to run Remmina on Windows, a guide is available on the official website.
FreeRDP
Website: freerdp.com
FreeRDP is a fork of the now-unsupported rdesktop project and is actively maintained under the Apache license.
FreeRDP is a terminal-based client. It is configured and launched entirely via the command line. Its command structure is similar to rdesktop, for example:
xfreerdp -u USERNAME -p PASSWORD -g WIDTHxHEIGHT IP
This command connects to the server at the given IP using the specified credentials and screen resolution.
KRDC
Website: krdc
KRDC (KDE Remote Desktop Client) is the official remote desktop client for KDE that supports RDP and VNC protocols.
It offers a clean and straightforward interface consistent with KDE's Plasma desktop environment.
KRDC is ideal for users of KDE-based distributions like Kubuntu, openSUSE KDE, and Fedora KDE Spin. It integrates well with KDE's network tools and provides essential features such as full-screen mode, session bookmarking, and network browsing via Zeroconf/Bonjour.
KRDC is actively maintained by the KDE community and is available through most Linux package managers.
GNOME Connections
Website: gnome-connections
Vinagre was the former GNOME desktop's default remote desktop client. GNOME Connections, a modernized remote desktop tool for GNOME environments, has since replaced it.
GNOME Connections supports RDP and VNC, providing a simple and user-friendly interface that matches the GNOME design language. It focuses on ease of use rather than configurability, making it ideal for non-technical users or quick access needs.
Features:
Bookmarking for quick reconnections
Simple RDP session management
Seamless integration into GNOME Shell
Connections is maintained as part of the official GNOME project and is available in most distribution repositories.
Apache Guacamole
Website: guacamole.apache.org
This is the simplest yet most complex remote desktop software for Linux.
Simple because it works directly in a browser — no additional programs or services are needed.
Complex because it requires one-time server installation and configuration.
Apache Guacamole is a client gateway for remote connections that works over HTML5. It supports Telnet, SSH, VNC, and RDP — all accessible via a web interface. Although the documentation is extensive, many ready-made scripts exist online to simplify basic setup.
To install:
wget https://git.io/fxZq5 -O guac-install.sh
chmod +x guac-install.sh
./guac-install.sh
After installation, the script will provide a connection address and password.
To connect to a Windows server via RDP:
Open the Admin Panel, go to Settings → Connections, and create a new connection.
Enter the username and IP address of the target machine — that's all you need.
The connection will now appear on the main page, ready for use.
Conclusion
RDP is a convenient tool for connecting to a remote machine running Windows or a Linux system with a GUI. The server requires minimal setup — just a few settings and firewall adjustments — and the variety of client programs offers something for everyone.
09 June 2025 · 6 min to read
Infrastructure
Docker Container Storage and Registries: How to Store, Manage, and Secure Your Images
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.
09 June 2025 · 6 min to read
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