Continuous deployment is a strategy in software development where code changes to an application are released automatically into the production environment.
A series of predefined tests drives the automation. Once new updates pass those tests, the system pushes the updates directly to the software's users.
Continuous deployment offers several benefits for enterprises looking to scale their applications and IT portfolio. Above all, it speeds up time to market by eliminating the lag between coding and customer value—typically days, weeks or even months.
In order to achieve this, regression tests must be automated, thereby eliminating expensive manual regression testing. The systems that organizations put in place to manage large bundles of production change—including release planning and approval meetings—can also be eliminated for most changes.
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Continuous deployment provides numerous benefits to organizations, including the following:
Automated testing—the most critical dependency for continuous deployment—occurs at each stage of the deployment pipeline lifecycle. This capability improves the overall quality of the deployment experience. For instance, automated testing can debug errors before they reach production.
Continuous deployment helps deliver updates and software releases quickly. Once new updates pass predefined tests, the system automatically pushes them to the software's end users.
Automated testing allows development teams to quickly and consistently deploy new features and improvements for enhanced customer experience.
Automating the deployment eliminates bottlenecks and reduces manual tasks. This process helps businesses save costs by reducing downtime.
Continuous deployment frees up developers to focus more on writing code and performing tests rather than manual deployment procedures. It also supports team collaboration and communication by providing a single view across all applications and environments.
Continuous deployment accelerates the feedback loop by allowing developers to release code changes frequently. This capability reduces the time that it takes to receive feedback from users and stakeholders.
While "continuous deployment" and "continuous delivery" might sound the same, they are two different approaches to frequent release.
Continuous delivery is a software development practice where developers build software to release it into production anytime. To accomplish this, a constant delivery model involves production-like test environments.
New builds performed in a continuous delivery solution are automatically deployed into an automatic quality-assurance testing environment that tests for any number of errors and inconsistencies. After the code passes all tests, continuous delivery requires human intervention to approve deployments into production. The deployment itself is then performed by automation.
Continuous deployment takes automation a step further and removes the need for manual intervention. The automated tests and developers are considered trustworthy enough that they don’t require approval for production release. If the tests pass, the new code is considered approved, and the deployment to production happens automatically.
Continuous deployment is the natural outcome of continuous delivery done well. Eventually, manual approval delivers little or no value and merely slows things down. At that point, it is done away with and continuous delivery becomes continuous deployment.
Check out this video in which Eric Minick with IBM Cloud explains the difference between continuous deployment and continuous delivery.
Another key element in ensuring seamless, continuous deployment is continuous integration (CI), the software development process where developers integrate new code into the code base throughout the development cycle.
In order for automation of deployment processes to work, all the developers working on a project need an efficient way of communicating the changes that take place. Continuous integration makes this possible.
Typically, when working on the same software development project, developers work off of individual copies of a main branch of code. However, functionality issues and bugs can occur after developers merge their changes onto the main codebase, especially when developers work independently from each other. The longer they work independently, the higher the risk.
With CI, everyone merges their code changes into a repository at least once per day. As updates occur, automated build tests run to ensure that any changes remain compatible with the main branch. This process is a fail-safe method for catching integration problems as quickly as possible.
For instance, unit tests validate individual components or functions, providing immediate feedback on code behavior. Integration tests assess interactions between software components and modules to ensure they're working together correctly and catch any issues unit tests might miss.
The continuous integration/continuous delivery (CI/CD) pipeline is an agile DevOps workflow focused on a frequent and reliable software delivery process. The methodology is iterative, rather than linear, which allows DevOps teams to write code, integrate it, run tests, deliver releases and deploy changes to the software collaboratively and in real-time.
The "CD" in CI/CD can be confusing as it is also used to refer to continuous deployment when describing ways to further automate down the pipeline.
Developers must use the appropriate tools to develop and deploy high-quality software improvements continuously. This process builds effective DevOps practices by ensuring efficient communication between developmental and operational departments. It also minimizes or eliminates errors in the software delivery pipeline.
Here are some of the most crucial tools and features used in a continuous deployment workflow.
Version control helps with continuous integration by tracking revisions to a particular project's assets. Also known as "revision" or "source" control, version control helps to improve the visibility of a project's updates and changes while assisting teams to collaborate regardless of where and when they work. Popular version control systems (VCSs) include Git, Bitbucket and AWS CodeCommit.
As simple as it sounds, "code review" is a process of using tools to test the current source code. Code reviews help improve the integrity of software by finding bugs and errors in coding and help developers address these issues before deploying updates.
CI is a critical component of continuous deployment and plays a major part in minimizing development roadblocks when multiple developers work on the same project. A variety of proprietary and open source CI tools exist, each catering to the unique complexities of enterprise software deployments.
Configuration management is the strategy and discipline of making sure all software and hardware maintain a consistent state. This strategy includes properly configuring and automating all servers, storage, networking and software.
Application release automation (or application release orchestration) is critical when automating all of the activities necessary to drive continuous deployment. Orchestration tools connect processes to ensure that developers follow all crucial steps before pushing new changes to production. These tools integrate with configuration management processes to ensure proper provisioning and the highest performance level in all project environments.
When operating a continuous deployment model, it's important to be able to visualize the data that lives in your testing environments or staging environments. IT infrastructure monitoring tools help you analyze application performance to see whether changes you make have a positive or negative impact.
Sometimes, a new software deployment to an environment behaves abnormally and requires a rollback to the previous version. Continuous deployment requires strong rollback capabilities. Organizations can use blue-green deployment strategies, which create two separate but identical environments to run different versions of an application, with the goal of minimizing downtime and allowing for rollback. Other rollback strategies include rolling and canary deployments.
DevOps teams use metrics to refine their processes in continuous deployment cycles. For example, a deployment frequency (DF) metric measures the frequency of code changes in a production deployment environment. This metric can help developers assess how quickly they can deliver new features to end users.
Kubernetes is an open source container orchestration platform that can greatly enhance the development of a continuous development pipeline. Because of its flexible, logical and intuitive user interface, Kubernetes makes it possible to reduce the common problems that arise when running into server usage restrictions and outages while supporting modern infrastructure and multicloud deployments.
By automating the deployment, scaling and operation of containerized applications, Kubernetes helps increase the agility of DevOps processes. Because of its modular design, Kubernetes allows the alteration of individual pods inside a service and seamless transitions between pods. This flexibility helps development teams avoid server downtime and allows for maximum resource utilization when running microservices.
Kubernetes is also an extremely reliable platform that can detect the readiness and overall health of both applications and services before deployment occurs.
Continuous deployment and related CI/CD platforms offer enterprise businesses cutting-edge solutions to automate software testing and deployment processes. Many of these platforms are infused with artificial intelligence (AI) and machine learning (ML) technologies that have transformed software development and deployment lifecycles. For instance, AI-driven predictive performance tools analyze metrics to make deployment faster, more efficient and less prone to errors.
These are examples of some of the top software platforms, services and tools for continuous deployment and CI/CD pipeline automation:
These platforms and tools support the following capabilities.
Many deployment automation platforms contain blueprint components that allow developers to create custom cloud environment models. These models enable DevOps teams to visualize application deployment in public, private, hybrid cloud or multicloud settings. Blueprint design tools allow users to create, update and break down full-stack computing environments while enabling full cloud orchestration capabilities. Developers can then provision all environments to deploy application components automatically or on demand.
Deployment automation platforms offer high scalability and support the dynamic deployment of mission-critical applications and services.
Being able to rely on the accuracy of automated testing environments is absolutely critical to successfully achieving continuous deployment. For some environments, however, creating conditions that flag manual approvals is necessary to ensure that the correct information is pushed to production at the right time. Deployment automation platforms offer deployment approvals and gates to give administrators more control, visibility and auditing capabilities over their continuous deployment processes.
While deployment automation platforms support the use of your own scripts, they also offer out-of-the-box plug-ins to make deployment processes easier to design and manage. By using tested integrations, developers can utilize prebuilt, proven automation.
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