QKS Group defines Cloud-Native Application Development Services (CNADS) as "a suite of services that...

With technological improvements, there has been a need for secure, faster deployment and a way to make software applications portable. Traditional approaches required shipping the dependencies together with the application to enable it to run on the target machine. Moreover, this approach had several challenges regarding security, dependency resolutions, deployment consistency and application portability. Enter containerization. Containerization can help you encapsulate an application with all its dependencies into portable containers. This isolation enables effortless deployment and scaling of applications. As containerization becomes more popular, the demand for container orchestration tools like Kubernetes has also increased. This article examines the architectural components of Kubernetes, how it works and why it is considered the de facto platform for deploying and managing containerized applications. ### An Introduction to Kubernetes Kubernetes, also known as K8s, is among the most frequently used systems for orchestrating containers. It is an open-source, cloud-agnostic platform and one of the most desired options for container orchestration because of its automated system for container deployment management and scaling features. To efficiently manage and optimize Kubernetes-based environments, you must understand the fundamental components. This knowledge will help you deploy your applications faster and scale while optimizing resource utilization for better performance. Using Kubernetes brings simplified management systems to containers, allowing applications to be aligned into efficient, reliable deployments. ### The Kubernetes Architectural Components The Kubernetes cluster contains two planes, namely: * Control Plane: This is used to manage the Kubernetes clusters and workloads that run on them. Typically, it comprises the API server, Scheduler and Controller Manager. * Data Plane: This represents the machines that can execute containerized workloads within a Kubernetes cluster. A cluster in Kubernetes is comprised of a control plane (master) and one or more workers or worker nodes. These worker nodes are virtual or physical machines. Figure 1 below shows the components of the Kubernetes architecture. ### The Control Plane Components Here are the components of a Kubernetes control plane at a glance: * kube-apiserver: This serves as the API server for the Kubernetes control plane and a single entry point for all communications with the cluster. The kube-apiserver coordinates the components in a Kubernetes cluster and enforces policies to ensure that the cluster state is maintained as desired. It implements both authentication and authorization control to allow only authorized users or applications to access the API. * etcd: This distributed key-value store in Kubernetes handles data storage of a cluster in a reliable, consistent manner. It is the brain of your Kubernetes cluster, serving as a consistent and highly available database as well as a backend service. It stores the state of your Kubernetes cluster, which includes resource utilization, object statuses, etc. The master node (or the control plane node) and other components in a Kubernetes cluster interact with etcd to read and write the cluster state. * kube-scheduler: This component oversees the assignment of pods to respective nodes after considering several factors, such as workload performance, hardware and software constraints, policy limitations and specified affinity and anti-affinity values. * kube-controller-manager: Most of Kubernetes architecture is based on the controller pattern. In Kubernetes, a controller is a component that constantly observes your Kubernetes cluster and triggers appropriate actions when an event occurs. The kube-controller-manager, as the name suggests, is a component of the Kubernetes control plane that manages all controllers in your Kubernetes cluster. The different types of controllers managed by the kube-controller-manager are: * Deployment controllers * Replication controllers * StatefulSet controllers * DaemonSet controllers ### The Worker Node Components The following is the list of components of the worker node in a Kubernetes cluster: * kubelet: In Kubernetes, each node has its own agent, known as kubelet, which runs on each worker node in a Kubernetes cluster. It manages the states of the pods inside the nodes in a cluster in Kubernetes. Each node must have its own unique kubelet, as pods cannot be created without it. * kube-proxy: This is a network proxy that contains the necessary intelligence to forward requests from services to the pods on the worker node. It is a network proxy that executes on each node in a cluster in Kubernetes, maintains network rules and facilitates network communication to the pods. * Container runtime: This represents the software that runs the applications hosted inside containers. Docker Engine is the most widely used runtime, but with Kubernetes, any runtime that complies with its Kubernetes container runtime interface (CRI) will work. ### How do the Kubernetes Architecture Components Work? The following is a step-by-step explanation of how the entire flow of Kubernetes architecture elements works: * As part of the initial step, the user sends a request for application deployment via the API server. * The API server accepts the user’s request, validates permissions and records the changes in the etcd. * The scheduler then distributes pods to the appropriate worker nodes. * The kubelet starts the pods via the container runtime after configuring them. * The container manager provisions or deallocates resources depending on the health of the pods on the worker nodes. ### Conclusion The Kubernetes platform brings together all deployment capabilities for managing distributed systems through containers. However, the architecture of this platform is complex. If you have a good understanding of the Kubernetes architecture components and how they relate to each other, it will help you design, deploy and manage applications on this platform effortlessly. This knowledge will also help you quickly identify and resolve issues when they occur. * Click to share on Twitter (Opens in new window) * Click to share on Facebook (Opens in new window) * Click to share on LinkedIn (Opens in new window) * Click to share on Reddit (Opens in new window) * ### _Related_

Kubernetes has become the de facto standard for orchestrating containerized applications, supporting legacy VM migrations and modern cloud-native workloads. As organizations scale, development teams frequently operate multiple Kubernetes application clusters — sometimes numbering in dozens, hundreds or even thousands. While this enables flexibility and autonomy, it also introduces significant operational complexity. Without centralized governance, organizations risk creating a fragmented Kubernetes landscape with varying distributions, configurations and maintenance strategies. This lack of standardization leads to inefficiencies, security vulnerabilities and excessive operational overhead. To counteract this, organizations often establish platform teams responsible for maintaining Kubernetes infrastructure, optimizing developer experience and ensuring the platform’s overall operability. While managing large-scale Kubernetes application clusters appears to be a well-recognized challenge, one often overlooked aspect is the complexity of managing databases and data services across these environments. ### The Data Service Challenge in Kubernetes Platforms Modern application development demands a diverse range of data services, including SQL and NoSQL databases, key-value stores, message brokers, document databases, search engines and analytics services. Developers expect these services to be available on demand, with self-service capabilities for provisioning, scaling, backups and restoration — all without the need for manual intervention from operations teams. For true agility, developers should be able to provision a new database instance at any time without external assistance. A well-designed platform should automate database lifecycle management, enabling developers to perform tasks such as backups, scaling and recovery through a seamless automation interface. However, achieving this level of automation in a large-scale Kubernetes environment presents several challenges. ### Key Challenges in Database Automation Across Kubernetes Clusters #### Diverse Automation Backends Databases must be automated differently depending on the environment. For example: * In air-gapped on-premises deployments, PostgreSQL databases may be provisioned as dedicated VMs, Kubernetes pods or clusters. * In public cloud environments, developers may use managed database services or third-party automation solutions like those offered by AWS or anynines. To support diverse infrastructures, a unified platform must integrate various automation backends, ensuring that databases can be provisioned and maintained easily across different environments. #### Security and Compliance Security is paramount when managing databases across multiple Kubernetes application clusters. Ensuring secure network connectivity between applications and database instances in large-scale Kubernetes environments emphasizes the need for well-integrated automation to manage databases and other data services securely and efficiently. Separation of concerns is a key success factor in database automation. Application developers should focus on developing applications and using databases, while platform operations teams should specialize in operating database automation. This clear distribution of responsibilities enhances operational efficiency, especially at scale where various developers, Kubernetes clusters, data service types and data service instances must be managed effectively. By adopting robust automation solutions such as a9s Data Services, which leverage open-source databases and data services, organizations can efficiently host their own databases while ensuring scalability, security and operational excellence. Proper automation enables simplified provisioning, maintenance and recovery, making self-hosted databases a viable option for organizations that require control over their data infrastructure. #### Operational Complexity Managing database automation at scale involves the following distinct responsibilities: * Application developers manage individual database instances, ensuring data integrity and application performance. * Platform teams maintain automation backends, troubleshoot large-scale failures, and enforce compliance with security and performance standards. Application workloads and database instances rely on ephemeral pods, persistent disks and scalable infrastructure. Given the dynamic nature of Kubernetes environments, network connectivity must be dynamically managed to ensure secure and reliable communication between applications and databases. ### A Centralized Approach to Database Automation in Kubernetes Platforms To overcome these challenges, organizations should adopt a centralized approach to database automation, treating database provisioning as a service rather than embedding database instances within application clusters. This approach offers several key benefits: * Improved Security and Governance: Platform teams handle database automation and security to ensure consistent compliance across the organization. * Multi-Backend Support: Applications can consume databases via Kubernetes operators, cloud-managed database services or third-party automation solutions. * Operational Efficiency: By decoupling database automation from application clusters, organizations reduce the operational burden on development teams and streamline management at scale. An example of an open-source project that addresses this challenge is Klutch, which provides a unified interface for managing database automation across multiple Kubernetes application clusters. Additionally, Klutch enables platform teams to standardize database provisioning, enforce security policies and integrate diverse automation backends — whether on-premises, cloud-based or hybrid environments. By leveraging Klutch, organizations can simplify the complexities of multi-cluster database and data service management while maintaining flexibility and compliance. ### Conclusion As the adoption of Kubernetes increases, managing data services across multiple application clusters becomes a critical challenge. While Kubernetes operators provide a native approach to database automation, they introduce operational complexities that may not scale effectively. A centralized database automation strategy that separates databases and data services from application clusters offers a more scalable, secure and efficient solution. This approach enables organizations to balance agility with security, ensuring that application teams can focus on innovation without being weighed down by database operations. By implementing centralized database automation, businesses can enhance compliance, streamline management and optimize their cloud-native infrastructure for long-term success. _To learn more about Kubernetes and the cloud-native ecosystem, join us at KubeCon + CloudNativeCon Europe, in London._ _KubeCon + CloudNativeCon EU 2025 is taking place in London from April 1-4.__Register now_ _._ * Click to share on Twitter (Opens in new window) * Click to share on Facebook (Opens in new window) * Click to share on LinkedIn (Opens in new window) * Click to share on Reddit (Opens in new window) * ### _Related_