Deployment Automation: Fast And Reliable Updates

Application Deployment Automation: Update With A Single Command

Deployment automation allows the software to be transferred from the test environment to the production environment – and vice versa – through automated processes: in this way, repeatable and reliable deployments are obtained for the entire application distribution cycle. This functionality is part of the typical DevOps concept of the CI / CD pipeline and refers precisely to the continuous deployment of artifacts.

This allows you to update the development, certification and production environments with a single command, favoring the speed and efficiency of the deployment. In a development model that does not follow the logic of DevOps, developers have to wait for feedback from the Operations department, with consequent slowdowns in the development process and delivery delays.

With The Nonstop Arrangement Notwithstanding, It Is Feasible To

1. Release new features faster and more frequently;
2. Test whether new features work as they should via unit or integration tests integrated into the deployment process. With the ability to set code coverage constraints and preventive deployment blocking;
3. Deploy the application in a natural environment and monitor its behavior when it interacts with other tools and dependencies, allowing the identification of even the most difficult bugs to intercept;
4. Work with automated tasks and reduce manual work, reducing error minimization rate and project cost.

DevOps Deployment Automation: Fewer Errors, Maximum Speed

The CD pipeline and deployment automation is divided into several phases:

1. compilation queues;
2. Unit tests;
3. Code analysis;
4. Installer creation.

If these steps are completed successfully, the executables are packaged into files stored in an artifact repository for later use. With automatic deployment, the quality of the test directly affects the quality of the releases: this is why its adoption primarily involves a change in the approach to software development.

The software distribution process is subject to several errors. Deployment is a process usually controlled by a shell script. There are often 15-20 different scripts created and managed manually by the developers. When an update is required, the time needed to ensure everything runs smoothly could be significant, albeit essential, to avoid incurring low performance or any blocks.

With A Continuous Distribution Instead

1. automated implementations are faster than manual ones;
2. nothing is taken for granted;
3. percentage of risk on the safety side reduced to a minimum;
4. Lower costs due to more efficient work of developers;
5. Faster MTTR (Mean Time To Repair) thanks to automatic rollback.

Software Deployment Automation Tools

There are several tools on the market for the construction and management of pipelines and, therefore, deployment automation, including open-source. The most used among the available solutions is undoubtedly Jenkins which runs containers and plug-ins, such as Apache Tomcat. Over 1700 plug-ins enrich the software integration, automation and distribution processes and provide a customizable environment. Being a tool that has been around for several years, it is suitable for legacy technologies and supports cloud-native applications.

GitLab, available on-premise and in the cloud, works exclusively with its repository system. It offers continuous distribution, manual distribution verification and rollback, fine-grained RBAC, comprehensive audit trails, and proprietary secrets management. Automatic deployment is included in continuous integration solutions, representing the second step of the CI / CD pipeline. The continuous integration process is undoubtedly more mature than the deployment and delivery part, but the platforms typically include both. 

Container Deployment Automation

It is good that the company first containerised the applications to obtain better results from deployment automation and, generally, from DevOps pipelines. CI / CD pipelines work much better with containerized applications. A container takes only a few seconds to start up and responds to the innovation needs of automation via a channel. Containerized applications don’t use as many resources as virtual machines, are faster, don’t require installation, and are highly portable.