Kubernetes is an open-source container orchestration tool designed to automate, deploy, scale, and operate application containers.
Kubernetes lets you run distributed systems resiliently, with scaling and failover for your application. It acts as a container orchestrator that helps ensure each container is where it’s supposed to be and that the containers can work together. There are a lot of applications that use the monolithic architecture; they put all the functionality like transactions and third-party integration into a single deployable artifact. In an inflexible monolith architecture, deployments can take a long time because everything needs to be rolled out together. If other teams manage different parts of your application, there can be a lot of complexity when prepping for rollout. Enabling a Developer Control Plane architecture, each resource and functionality is split apart into smaller individual modules or artifacts. If there’s an update, only that exact module will be replaced.
When working with Kubernetes and Cloud-Native tools, many teams and developers are bothered about tooling. There are a lot of tools in the Kubernetes space, and it’s very challenging for engineering teams and developers to pick and select the best tools, especially given that in the last years, there has been a tooling recline in Kubernetes development processes.
Managing complexity and scaling up your resources can be a pain, especially when you are unsure about the number of resources you will be running and the Memory and CPU usage it will be consuming. Container testing becomes more complex as it moves toward your production environment.
Selecting the best tool for your Kubernetes workload is challenging.
Businesses require new features and changes all the time. Adopting and selecting the right tool that solves all your business needs is really demanding because you have a never-ending list of tools.
Securing your application build pipeline depends on every dependency and tool you install. Kubernetes doesn't give you a cloud-native production stack, and there are several other tools you need to build around it, from package managers, monitoring tools, etc.
More open-source tools and software are announced every year, and implementing and working with those tools can be very difficult and challenging most times, especially when you are new to the Kubernetes and Cloud Native Space.
Combining components of your application like the frontend and backend (which will be serving the APIs) and your microservice connecting your frontend to your backend layer requires a lot of high-level tools because of your API capabilities, scalability, and performance issues. Breaking each component down into a pipeline requires understanding what each component needs and how they can integrate into the same workflow together. Any missed step won’t let you offer some level of discipline around how you build, deploy, and test.
Developers spend a lot of time coding and packaging your application, writing many
Yaml files, and building your containerization workflow. At the same time, when you’re done coding and packaging your application, you are deploying your different environments (Dev, Staging, and Production) and forwarding your application to a managed Kubernetes service or a local cluster on your machine. After forwarding and deploying your application to your cluster, how do you release your application? In the release cycle phase, you will experience many services based on your Infrastructure needs and resource configurations. Choosing and working with the right CI/CD tool will benefit you here.
Adopting the right tools
The heart of your application development is your application workflow process. This process has to be architected in a significant way to avoid complications and issues during implementation. As Developers and Engineers, it’s a top requirement for you to adopt the best tool for your workloads and resources into your workflow. Switching tools has a high impact on your Organization, and you will not be able to address dynamic market demands. Consistent changes can significantly impact your resources, and you need to interact with them regularly. Ideally, you want to have one consistent way of implementing and working with your workloads in Kubernetes and integrating them with GitOps.
For example, in building a Cloud Native application, you will want to make use of a Pipeline that will consist of the number of stages that will take us through the lifecycle of building and packaging your components.
CTO.ai architecture helps you manage your applications that are made up of hundreds of containers in different environments.
Check out how we enable engineering teams to set up a Developer Control Plane on CTO.ai below.
The CTO.ai product provides you with all the Kubernetes offerings from packaging, monitoring, deploying, scaling, automating, etc.
The CTO.ai Developer Control Plane joins and connects each functionality from coding, shipping, deploying, running, and releasing your Applications into a configured Developer Control Plane for you to use. All configurations are accessible with ease, and you can understand which versions of your applications will be deployed and released and monitor and understand issues before they arise. We give you consistent Developer Experience across the whole important part of the software development lifecycle, thus enabling better products and faster delivery.
CTO.ai can provide a consistent Developer Experience across the entire important part of the software development lifecycle, thus enabling better outcomes and quicker delivery. We enable teams to easily integrate their existing tooling into a unified workflow that provides both development tools and application hosting infrastructure so your entire team can confidently deliver to any managed cloud environment on time and on budget.
Our Developer Control Plane teams, processes, and products come together to enable continuous delivery of value to your end-users. The CTO.ai Developer Control Plane will accelerate your business, improve your customer satisfaction, and help you achieve the high stability and scalability of your application's setup.
You can also integrate DORA metrics into your workflow to deploy more and gather specific insights into your data like frequent code deployments, lead time from commit to deploy, time to recover from incidents, and change failure rate. CTO.ai enables you to spend less time configuring and deploying your workflow because the underlying infrastructure is already configured for you. You can focus more time on your business value and products.
Now that you’ve understood how to set up and build a Developer Control Plane on CTO.ai, check out how you can leverage the CTO.ai developer control plane to deliver your resources and engineering teams with an intuitive PaaS-like CI/CD workflow that will enable even the most junior developer on your team to deliver on DevOps premises.