In this article, let's take a look at 7 best DevOps tools, from automated build tools to application performance monitoring platforms...
DevOps culture is now an integral part of every tech-savvy business and plays a role in many business processes, ranging from project planning to software delivery.As cloud services are prevailing today, the requirement of related supplementary services is growing rapidly. DevOps technologies are increasing as well, so how one should choose the right tools to automate work? There are a lot of opinions.
There are a lot of tools that make DevOps possible, and it will be near impossible to cover them in one article. However, the 7 tools you’ll learn about in this article are some of the most popular and powerful DevOps tools.
A lot of DevOps engineers call Jenkins the best CI/CD tool available in the market, since it’s incredibly useful. Jenkins is an automation server that is written in Java and is used to report changes, conduct live testing and distribute code across multiple machines. As Jenkins has a built-in GUI and over 1,000 plugins to support building and testing your application, it is considered a really powerful, yet easy to use tool. Thanks to these plugins, Jenkins integrates well with practically every other instrument in the continuous integration and continuous delivery toolchain.
Easy to install and a lot of support available from the community.
1,000+ plugins are available and easy to create your own, if needed.
It can be used to publish results and send email notifications.
Terraform is an infrastructure-as-code tool that lets you build, change, and manage infrastructure properly. You can consider Terraform to be a provisioning tool. It helps you set up servers, databases, and other kinds of infrastructure that powers full-scale applications.
The code that manages infrastructure using Terraform is written in the Hashicorp Configuration Language (HCL). All of the configurations you need should be in this file as it will include dependencies to let the application run. HCL is declarative, such that you only need to specify the end state you desire, and Terraform will do the rest of the job.
Terraform is not restricted to any particular cloud service provider as it works with multiple cloud providers and environments. There are no issues with compatibility when using Terraform. Cloud services providers such as AWS, Microsoft Azure, Google Cloud all integrate seamlessly with Terraform. Version Control System hosting providers such as Github and Bitbucket all also work fine with it.
There is an enterprise and open source version and Terraform can be installed on macOS, Linux and Windows systems.
Similar to Terraform, Ansible is also an infrastructure-as-code tool. Ansible is a tool that helps with the deployment of applications, the provisioning and configuration management of servers. Ansible is built in Python and maintained by RedHat. But it remains free and open source.
As a configuration management system, you can use Ansible to set up and build multiple servers. You get to install Ansible on a control machine, without requiring Ansible running on the other servers which can vary from web to app to database servers.
Unlike Terraform, Ansible doesn’t make use of HCL for its code. Instead, the configurations are written in Ansible playbooks which are YAML files. Ansible uses a hybrid of a declarative and procedural pattern. This is different from Terraform, which is solely declarative.
Since Ansible works on a control machine that administers others, it requires a mode of communicating with them. In this case, Ansible uses SSH. It pushes the modules to the other servers from the dominant server. Ansible is an agentless system, as it doesn’t require a deployment agent on the different machines.
Linux is the most suitable operating system for installing Ansible. However, it also works fine on macOS. For Windows users, it is possible to use Ansible through the bash shell from the Windows Subsystem for Linux.
Docker is a software containerization platform that allows DevOps to build, ship, and run distributed processes within containers. This gives developers the ability to create predictable environments that are isolated from the rest of the applications and can be run anywhere. Containers are isolated but share the same OS kernel. This way you get to use hardware resources more efficiently compared to virtual machines.
Each container can hold a single process, like a web server or database management system. You can create a cluster of containers distributed across different nodes to have your application up and running in both load balancing and high availability modes. Containers can communicate on a private network, as you most likely want to keep some of your application parts private for security purposes. Simply expose your web server to the Internet and you are good to go.
What I like most is that you can install Docker on your computer to run containers locally to make some ad-hoc software tests without installing its dependencies globally. When you are done, you simply terminate your Docker container and your computer is as clean as new.
Build once, run anywhere! You can package an application from your laptop and run it unmodified on any public/private cloud or bare metal server.
Containers are lightweight and fast.
Docker Hub offers many official and community-built public Docker images.
Separating different components of a large application into containers have security benefits: if one container is compromised, others remain unaffected.
Kubernetes (K8s) is a Google open-source tool that lets you administer Docker containers. Since there are often a lot of containers running in production, Kubernetes makes it possible to orchestrate those containers.
It is, however, important to understand the reason to orchestrate Docker containers in the first place. When there are many containers running, it is hard to manually monitor these containers and have them communicating with each other. Asides, this scaling also becomes difficult as well as load balancing.
With Kubernetes, it is possible to bring all these containers under control so this cluster of machines can be administered as one machine. Often compared to Docker Compose, Kubernetes is different as it makes it easier to deploy, scale, and monitor the containers. When any of them crash, they can self-heal, and Kubernetes can spin up new ones as replacements. With K8s, it is possible to do storage orchestration, service discovery, and load balancing easily.
You can install Kubernetes on macOS, Linux, and Windows and use it through the Kubernetes command-line tool.
RabbitMQ is a great messaging and queuing tool that you can use for applications that runs on most operating systems. Managing queues, exchanges and routing with it is a breeze. Even if you have an elaborate configuration to be built, it’s relatively easy to do so, since the tool is really well-documented. You can stream a lot of different high-performance processes and avoid system crashes through a friendly user interface. It's a durable and robust messaging broker that is worth your attention. As RabbitMQ developers like to say, it’s "messaging that just works."
Guaranteed message delivery.
Push work into background processes, freeing your web server up to handle more users.
Scale the most frequently used parts of your system, without having to scale everything.
Handling everything with ease even if it seems to be a huge crash.
Packer is another DevOps tool from Hashicorp on the list. Written in Golang, Packer helps you automate the creation of virtual images. The process of manually building images can be frustrating as it is error-prone, but Packer eliminates all of that.
With a single JSON file, you can use Packer to create multiple images. So when it works the first time, there’s a guarantee that it will work the hundredth time since nothing interferes in the automation process. Many cloud service providers work with images, so you can seamlessly work with those providers since Packer standardizes the creation of images for the cloud environments.
Packer doesn’t work as a standalone tool. You can integrate it with Ansible, Chef, and Jenkins so the images can be used further down in the deployment pipeline. The installation process is not complicated, and you can learn to get started with the tool.
The concept of DevOps is can be very beneficial to getting large-scale applications to be performant under different kinds of load or traffic. It also makes the software deployment pipeline easy to manage. However, the concepts DevOps are hard to implement without the availability of tools. There are many tools in this space and companies have varying choices.
Thanks for reading
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