I use the solution in my company for internal applications and deployments in the DevOps area. The tool's main use cases revolve around areas like research and development tools and CI/CD pipelines.

The most valuable features of the solution are the support I get from Windows and Linux, along with the cloud platforms and the CLI. There is also a repo feature in the tool.

The backup compatibility for legacy window stations is an area of concern where improvement is required, especially since my company has certain legacy tools that we are struggling to move. In our company, we are still unable to move certain things to Docker.

Docker should do something the same way GitHub has added AI and Aline, which can basically do a lot of work for users.

I have experience with Docker.

It is a very stable solution. Stability-wise, I rate the solution a ten out of ten.

There are no scalability issues with the product.

There are no challenges in scaling up the product for larger applications.

I work in a big company, and I only know that my team uses the product, but I guess there might be a lot of people who use it in our organization.

The product's initial setup phase is straightforward.

The product's deployment phase was easy as we had to just install it in our company.

Two engineers are required for the deployment and maintenance of the product.

The solution can be deployed by me in our company.

The product has helped with the cost-savings part, especially since our company operates in a repeatable environment where it is easy to deploy the tool for multiple users.

Though I don't know about the payments associated with Docker, I know that it is done centrally by your company.

My company considered a product named Automaton against Docker. My company did not like Automaton. Docker is more commonly available and well-documented, and it is easier to get a lot of information about it on the internet.

In terms of the impact of container orchestration, I would say that though my company does not currently use a lot of containers, we plan to move to containers.

I recommend others to just use the internet than ChatGPT since mostly all issues are already known and very easy to deal with so it is better to use the knowledge from the industry.

In terms of the use of Docker for AI projects or workflows, I would say that my company deploys AI applications on Docker, like any other application and pipeline.

The tools or practices that have proven to be the most beneficial for our company's AI initiatives are agnostic to Docker. There is nothing special in Docker, and our company just uses it to deploy applications.

The value derived from the use of the product revolves around the scalability and the reliability it offers. The product helps with the cost-savings part since it saves time and money linked with the amount of engineers required to spend time on operations.

I rate the solution a nine out of ten.

We are a service provider and we have various use cases. However, the most prominent one is that instead of virtualizing the application workloads, we use Docker. Docker allows you to create small applications and containerize them. You can create multiple such application containers that can run simultaneously on the Linux operating system.

We use Docker because if we want to create an application using Node.js, an environment for backend and frontend technologies, we can deploy this single application in a container. This container will run independently of its underlying OS. 

If you want to migrate this application to a different server, migrate the container image to the new server and start using the application there. This makes developers' jobs much easier because they don't have to copy everything the application needs. They just copy the container image, send it to a different server, and start using it there. All the required libraries, packages, and dependencies are encapsulated within that container. The container contains everything it needs to run. In a DevOps environment where multiple teams are creating applications, developing new versions, and deploying applications frequently, Docker is very helpful.

The greatest benefit of the solution is that you can create applications and multiple versions of the same application, distributing them across various Linux platforms. Setting up Docker is very quick; whereas setting up a Linux operating system can take five to ten minutes, you can set up a Docker-based Linux OS container within minutes. Docker also has a small footprint on memory and resources because it uses the system's resources efficiently. It operates within its environment, independent of the underlying operating system, running in its namespace. This isolation prevents negative impacts on the system.

For developers who frequently create and deploy applications, Docker makes it easy to quickly develop, deploy, and run the latest versions. You don't need to manage the operating system, applications, dependencies, or data separately; everything is encapsulated within the Docker container itself.

Moreover, AI is another application you can use through Docker. AI can help you set up more efficient Docker-based workloads. The role of AI is to guide you and provide the necessary knowledge for various tasks, whether it’s related to Docker or anything else. Additionally, AI itself can run as a container using Docker.

There's always room for improvement. Sometimes there's a bug or defect in the solution. However, the Docker team works on fixing it.

The solution’s stability is great. I rate it a nine out of ten.

Regarding scalability, Docker doesn't require any separate configuration for multiple users. Suppose you create a container and expose it to the public via an IP address and port. The ability to serve the number of users depends on the system's resources, like RAM, CPU, and storage. You can create or scale the number of containers based on these resources, and manage the number of users accordingly.

For example, if I have a small server, I can create containers and share them with the outside world according to the available resources. The server's ability to handle the number of users depends on its resources. Docker doesn't restrict you to a certain number of users; hundreds or even thousands of users can access the containers. Most of our customers are using the product.

The initial setup hardly takes minutes. All the container images are available on Docker Hub when you create a container. Docker Hub is a repository, like an App Store for containers. If you use Windows or Mac, you have an App Store where you can download and start using any application you want. Similarly, Docker Hub is the application store for containers. It's a cloud-based solution. In Docker Hub, you can find all kinds of container images. If you want to install an Apache server, a Postgres database server, or any other server—even Linux and Windows servers—they are all available in containers. Anyone who wants to use these can immediately download and start using the container image.

Our customers buy the software and the prices are available on Docker Hub. The subscription is typically yearly, and customers will need to buy the subscription directly. They can purchase it themselves.

The product is similar to GitHub.

If you are trying to create an application quickly for testing or development purposes, Docker is the best tool available. Docker can also be used on Mac and Windows with a tool called Docker Desktop. You can start using Docker there by installing Docker Desktop on a Windows or Mac system.

Whenever a new Docker image is created by the OEMs, like MySQL for instance, they name the new image with the version number. If you want to check the latest version, you can go to Docker Hub and search for it. There are multiple versions available, and Docker also allows you to create your own version. So let's say your company has specific requirements for MySQL. You can download the original MySQL image from Docker Hub, install a couple of additional applications within the Docker container, and share this customized Docker image via your own repository on Docker Hub.

There are two kinds of updates: one within the Docker technology itself, where the creators of Docker keep updating the Docker engine, and another for the Docker images. The Docker engine is the software tool that runs on your system, and the global team working on it keeps it updated. You can always check for the latest version of the Docker engine and upgrade as needed.

Moreover, container orchestration is a different technology. You need Kubernetes to orchestrate the containers in the solution. A single container is independent, but Kubernetes is an orchestration tool that manages these containers using its control and data planes. For example, if you have hundreds of containers, it takes work to manage each one independently. You need some orchestrator to control these containers' life cycles, maintain high availability, and more. Kubernetes handles this container orchestration.

Overall, I rate the solution a nine out of ten.

In our company, I have used Docker to launch an application publicly. The aforementioned application becomes available to end users via CloudFront. Docker is also used to cache the optimized performance.  

Docker hasn't been directly able to improve our organization's deployment processes. 

Docker offers a resilient system with minimal failure chances and optimized performance. For example, if an EC2 is utilized, the software container can be customized to allow only the specific benchmark of resources to be used. I found the solution to be easily customizable. 

The vendor should release a monitoring tool or include monitoring capabilities in Docker that can be used to monitor container performance and other Docker-based solutions. 

I have been using Docker for two years. 

I would rate the stability a nine out of ten. As containers are isolated and not dependent on each other, stability is satisfying in Docker. With the solution, when one service is down at the production end, it won't affect the functionality of other services running in parallel. The solution can be considered stable 99% of the time. 

I would rate the scalability an eight out of ten. Docker offers satisfying scalability features, and it depends upon the correct configuration being run within the virtual machines. Scalability in Docker is easier to manage than in legacy-based documents.

Presently, Docker is used by only me in our organization. Docker is an open-source solution. In our organization, we work with Docker on average once a week. At our company, we are not planning to increase the usage of Docker. 

Previously, applications were directly deployed in the server in our company instead of packaging the applications and running them on isolated environments. For instance, if any application experiences outage, it affects the other services as well, but with Docker this issue doesn't arise. 

I would rate the initial setup a ten out of ten. Docker offers an effortless setup process. As part of the setup, I have faced some configuration failure issues in Docker compose utility, but I was able to resolve it on my own with some changes in configuration. A feature of Docker can be made live in 20 to 30 seconds but the total deployment of the solution took me around one and a half days. 

As part of the deployment process, I inputted the code in the local environment and tested it on my personal computer using the Docker desktop utility. Following the aforementioned step, I pushed the code to the cloud and cloned it in EC2. Then, I developed a few necessary configuration files for Docker deployment and building.

As the last step of the deployment, the commands are run successfully to make the solution go live. Only one professional is enough to deploy Docker. To implement maintenance for a production-based cluster, a team of three professionals, one lead and two other team members, is needed. 

It's an open-source solution, so it doesn't cost anything. 

Docker is a trending tool and is being used for numerous production based applications, so instead of evaluating multiple tools, our company adopted Docker. 

I find Docker to be a user-friendly product. Docker acts as a stand-alone application without any dependency on other hardwares, so it can be used in an isolated environment. The tool can be easily customized. 

In our company, the product is running on EC2. I have restored all the required packages and need to compile the code within a container using the Docker tool. After the aforementioned step, Docker is used to run a few specific images where images are not dependent on each other. If one of the running images on Docker malfunctions, there won't be any cases of outage because the environment remains isolated. There is no interdependency between containers in Docker. 

Docker's container orchestration capabilities support our organization's projects with minimal downtime and low failure chances and offer a robust system. Our company has a Docker compose utility to customize the workflow and make it operate smoothly. Overall, I would rate the solution a nine out of ten. 

We use the tool for some of our services. We use it for containerization.

Load balancing features are valuable. We are leveraging this software for containerized services. We are using EKS for orchestration. We are leveraging almost all features. The solution helps save costs. We can know how much CPU memory is required so we can manage it accordingly.

Docker management is very difficult when we have a large number of nodes.

I have been using the solution for the last two years.

I rate the tool’s stability a six or seven out of ten.

I rate the tool’s scalability a six out of ten. We have more than 500 users. The tool is used extensively. We have plans to increase usage in the future.

We get good support from AWS.

The setup is straightforward. We need ten people for deployment, including managers, developers, and the IT team.

We can deploy the product ourselves.

We use the open-source version.

We use EKS for large applications. I will recommend the solution to others. If people want to leverage load balancing and containerized services with less CPU and load management, they can use Docker. We must pay for EKS based on the clusters and nodes we use. The price depends on the use case. Overall, I rate the solution an eight out of ten.

We use Docker for validation mechanisms and have built Docker images for our applications.

For container orchestration, we haven't used Kubernetes instances extensively. Docker works well for us.

It's flexible and easy to use. My team also finds it comfortable to work with.

Sometimes, it can become heavy and cause performance issues.

The stability is pretty good. 

We use horizontal scaling, and it works well. Considering its container architecture, I'd rate the scalability a nine out of ten. 

There are probably around 2,000 to 3,000 end users. It is extensively used. 

We have used Docker's technical support. I didn't have bad experience with the customer support. 

We used Kubernetes and other packaging techniques. 

Portability and accessibility were the main reasons we chose Docker. 

The initial setup is mostly straightforward. We used Ansible for deployment.

The ROI is high. Traditional deployments take more time and builds can be inconsistent across different environments. Docker solves these issues.

The value is there. Docker delivers high performance, leading to increased productivity. You can achieve more in less time, and your deployment cycle time is reduced.

I would rate the solution an eight out of ten.

AI with Docker is relatively new for us. But for traditional deployments like microservices and Spring Boot applications, Docker works very well.

I definitely recommend it. The landscape is changing, and Docker is transforming the way we approach deployments.

Instead of building images for underwriting systems, we pull pre-existing standard images. This allows us to quickly set up the necessary environment for development. For example, if I need a database instance, I simply pull the Docker image and create it, rather than going through a full installation and build process. We also leverage these images to create small, stackable components for building solutions. This streamlines our workflow and enhances our ability to upgrade and adapt quickly.

Docker setup itself is a useful thing because Docker itself is running your environment in a separate isolated box. So, it's not that things will be deployed or installed on your local system. Things (as in the development environment) will be separate. 

Then, the development environment can be segregated from the local underlying hardware. Your instances can be separate, or whatever you build, the application itself can be covered in one single Docker image at the end of it. All these things can be done. It saves part of the configuration and installation time of the initial local setup of the development team. 

So whenever I'm developing something, the time required to set up the local environment and the time required to set up other developers' environments will be radically reduced. So that is a great thing that we have.

TL;DR, Docker helps to reduce the time to set up the environment.

Important features for CI/CD workflows:

For CI/CD pipelines, instead of building separate deployable files, we package our solutions as Docker images. This allows us to compose the entire solution using Docker, which improves our deployment process. 

We create Docker images for each build and push them to a registry. These images are then pulled and deployed in our environments, streamlining the entire process.

Docker volumes. When I integrated volumes, I faced challenges. I have not essentially used add volumes to the existing instances. Maybe, I am not familiar with adding volumes to the existing Docker images. That might be a challenge, but it should be fairly straightforward. I'm just not aware of it. So just for me personally, it is a challenge.

Docker has all the major support already. They should publish all the build images of Docker on an open-source resource like Github. They should support all the stuff.

For example, now Docker supplies all these standard images, like database images or whatever image that the standard application is related to. Whenever there is Docker Compose information available on an open-source community project, that image should also be maintained and supported by Docker itself instead of the dev team doing it. Docker should take an interest in building those images and maintaining them for the public community projects that are hosted.

I have been using it for more than four years now. 

There were some issues with Docker images. In the Alpine image, I downloaded the one. There were some packages that you are not able to deploy on specific Docker-based images.

It is not directly a problem with Docker itself, but with the base image that it is starting. Like the Alpine image, the Linux image being used as a base for Docker. That was not compatible and had some package issues, which prevented us from deploying our solution on top of it. All the libraries we had were not going clean on it.

There needs to be a support matrix to identify which Docker base image can support which packages. The package team should tag these things to those base images, indicating which base images are supported. Once they have this matrix, both the package and the Docker base machine should have it.

It is difficult for a developer to understand and see a problem at the end once you develop and deploy. You stumble upon these problems, then you fix them. It takes a lot of time. You need to upgrade, rebuild, and rearrange. That is the only issue I want to clarify.

There should be a mechanism to identify which images should be supported.

Docker is quite stable. I don't see an issue there. I never saw a Docker image going down.

I don't see any issues. At least from my experience, I don't see anything going wrong with an image that I have used.

For stability, I mean it is up and running for a long time without any issues on the Docker side.

Overall, I would rate it a nine out of ten. 

Docker is very good at scaling up applications. There is no problem with scaling. I'd rate it a nine out of ten. It's good. 

In my earlier company, almost the entire team used Docker. The last team I had a year ago had eight members, and we all used Docker images for our development.

There is a good amount of documentation that quite helps. Every question that I had is addressed in the documentation or on the forum. I've got all the answers I needed. 

I didn't have a chance to raise a specific issue or interact with support directly. Everything is documented, and there's a lot of information available, so I didn't need to raise a support ticket.

The initial setup is quite easy. I don't find it difficult. 

For Windows, it can be a bit confusing, but for Linux, it is quite easy. Both Linux and Mac can do it very easily. However, for Windows, I did face some challenges, it doesn't work as smoothly on Windows.

For Linux and Mac, I would rate my experience with the initial setup as a nine out of ten, with ten being easy. For Windows, I'd give it around six to seven.

• Deployment model: Mostly, everything is on-premises only. We have not used anything on the public cloud.

• Deployment time: That depends upon the application setup and the dependencies. Generally, Docker itself does not take much time. It’s purely based on the application and its dependencies. So, it should not take more than a few minutes. Two minutes, maybe a maximum of sixty seconds. It's quite fast.

ROI is there in terms of efficiency. Adding the development time setup or the build time required is quite easy, and we always eliminate the environmental differences between one environment and another. 

For example, the underlying machine can have anything, and the developer can have anything, but still be able to run the application or whatever is being developed. It eliminates issues with the underlying hardware or operating system.

The offering and service are quite free. We have used only the free instance, and we are also on-premises only. So there's nothing that we have had to pay for.

Docker is the most prominent one. I didn't see any other choice there. But after Docker, Kubernetes started kicking in, and we are using some Kubernetes as well. However, we still use Docker images. 

Docker and Kubernetes are not parallel but complementary. We are ramping up with Kubernetes and understand how we can orchestrate it. 

It is a quite mature technology. People can simply use the existing features available, which will really help their development and make it much easier. Definitely, it will have all these benefits built along with it.

Overall, I would rate it a ten out of ten. 

Currently, we are creating a JAR file and using microservices. There are around 178 services in a single project. We use Docker to manage and do load balancing for all the services together.

We are using Docker to host applications. We are using Docker Compose and sometimes Docker Screen.

We are facing multiple issues with the solution's stability.

We have been using Docker in the same project for five years, and we have recently deployed Docker into Kubernetes for four years.

We are facing multiple issues with the solution's stability. It is very difficult to analyze how much load a tool takes. Sometimes, our Docker images get crashed due to the unavailability of space.

I rate the solution a six or seven out of ten for stability.

Approximately, 400 users are using the solution in our organization.

I rate the solution a nine out of ten for scalability.

The solution’s initial setup is simple.

We implemented the solution through an in-house team. It took me around three to five hours to deploy the solution. Docker is easy to use and implement. For the deployment process, we used Docker in the CI/CD pipeline. Then, we pushed the Docker images to the Kubernetes as per the environment. We used Kubernetes to host and balance the load.

Since Docker is an open-source solution, it is free of cost.

Docker made it easy for us to monitor the microservices. Secondly, we were able to manage the key load balancer. Thirdly, we wanted to create a secure layer of protection for our organization. So, we started using the containers above the hypervisor.

It was like using a new operating system. We created our own network adapters. We created another isolated network inside the Docker containers to keep our organization and project safe and secure. We consider this very helpful in the secure software development life cycle.

We're implementing multiple stages in security. We are signing Docker images, Docker containers, Kubernetes containers, Kubernetes nodes, and clusters in cryptography. We are signing these so that no one tampers with the code or the docker images. We are doing this from a security point of view.

The solution's containerization helps us in providing integrity and availability. If you use a microservices architecture, I recommend implementing Docker and Kubernetes. I would recommend Docker and Kubernetes to manage multiple microservices running simultaneously.

We had to configure multiple services, like the utilization of the CPU and the utilization of the RAM for the service that we were building. Additionally, we had to mention the network adapters we will be using. We followed multiple benchmarks, like NIST Benchmarks and CIS benchmarks.

There were multiple advantages to using Docker. The most important advantage was scalability and availability. As I am working in the security team, I'm looking after the integrity of the Docker images.

Overall, I rate the solution an eight out of ten.

I use Docker for containerization, deployment, and to create packages. Docker has a wide range of uses and integrates well with other command-line tools like Terraform. Docker is most helpful when trying to work with CI/CD pipelines.

Docker's containerization property is its main selling point.

I'd like to see the new update on containers and what you can achieve with the new version of Docker by using containers. I would also like to know the range of applications for which it can be used. I want to see if new architectures have been incorporated in Docker or if we have to wait for another release.

I have been using Docker for one year.

I rate Docker a nine out of ten for stability.

Docker is a very scalable solution. I rate Docker a nine out of ten for scalability.

Docker's initial setup is straightforward. As a command line tool, it also provides you with docks to reference if you get stuck or have issues.

The solution's deployment time depends on your internet latency or internet speed and the size of the container. If you are deploying a very large application, it will take some time to set up.

Docker is deployed on-cloud in our organization.

To effectively use Docker, users should learn the basics of Docker and understand what containers and images are in Docker.

Overall, I rate Docker a nine out of ten.

We set it up a lot of times to create environments for our students. 

When we teach our students, it's really important that everybody is on the same version of all their dependencies. So, Docker, we can set up the images on Docker, and it sets everybody up. 

So it's a much smoother process. Without Docker, without these containers, it would be a nightmare trying to get 20 to 30 students to set up their environments correctly. So this is just a really quick and easy way to use it.

Just the portability of Docker's containers and its ability to give everybody the exact same environment and experience is most valuable to me. 

It could be easier to create images and save them on reports.

Just improved development workflow, just quicker, like some better user experience creating the images for the Docker containers.

We have used it every day in my current company just to set up our environment for our build processes. We create a lot of labs.

We use the latest Docker version 4.22.

It is a very stable product.

It is extremely scalable. I would rate the scalability a ten out of ten.

The initial setup was very easy. I use it on-premises as well as in the cloud. So we use it in both places.

It's free, as far as I know. 

Docker is very reliable. It's easy to scale, and it's very stable.

Overall, I would rate the solution a nine out of ten. 

We work with containers for forecasting. 

The solution's most valuable feature is the Dockerfile, which makes the life of developers easy. Developers get a real understanding of container creation. 

I have encountered network issues while connecting with microservices. I want to optimize the application layers over the bucket. It should also have better documentation. 

I have been working with the solution for seven years. 

I rate the tool's scalability a nine out of ten. 

We solve issues using the documentation. 

Neutral

Docker's implementation is straightforward. However, it can get a bit complicated when you are doing sophisticated implementation. 

We have seen ROI with the tool's use. 

Docker's price is good. 

I rate the product an eight out of ten.