Kubernetes: Automating Containerized Applications

In the world of modern application development, managing containerized workloads at scale presents significant challenges. Teams grapple with ensuring high availability, seamless scaling, and efficient resource utilization across distributed environments. Without a robust orchestration layer, these tasks quickly become a source of operational complexity and manual toil, hindering agility and increasing the risk of downtime.

This article dives deep into Kubernetes, the open-source platform that has become the standard for automating container deployment and management. We’ll explore its fundamental concepts, architectural components, and how it empowers organizations to run resilient, scalable applications. Furthermore, we’ll examine how Kestra integrates with and extends Kubernetes, offering a declarative, polyglot orchestration control plane that streamlines complex workflows across your entire stack.

What is Kubernetes?

Kubernetes is an open-source platform designed to automate the deployment, scaling, and management of containerized applications. Often abbreviated as “K8s” (K, 8 characters, s), it groups application containers into logical units for easy discovery and management, providing a robust framework for running distributed systems resiliently.

Kubernetes definition and core concepts

At its core, Kubernetes is a container orchestrator. It takes a collection of physical or virtual machines and turns them into a unified, powerful computing resource. Developers can then deploy their applications without needing to worry about the underlying individual machines. Kubernetes handles critical tasks like resource allocation, load balancing, self-healing (restarting failed containers), and scaling applications up or down based on demand.

The platform operates on a declarative model. You define the desired state of your application—for example, “I want three instances of my web server running with this container image”—and Kubernetes works continuously to maintain that state. This approach aligns perfectly with modern Infrastructure as Code (IaC) and broader infrastructure automation practices.

The history and evolution of Kubernetes

Kubernetes has its roots in Google’s internal cluster management system, Borg. For over a decade, Google ran its production workloads, including services like Gmail and Search, on Borg. In 2014, leveraging the experience gained from Borg, Google open-sourced the Kubernetes project.

The project was donated to the newly formed Cloud Native Computing Foundation (CNCF) in 2015, where it quickly became the flagship project. This move to a neutral, vendor-agnostic foundation fueled its rapid adoption across the industry. Today, Kubernetes is one of the most active and largest open-source projects in the world, with contributions from thousands of individuals and major tech companies.

Key features and components of Kubernetes

Kubernetes has a rich set of components and abstractions that work together to manage containerized applications. Understanding these is key to using the platform effectively.

• Cluster: The foundation of Kubernetes. A cluster consists of a set of worker machines, called Nodes, that run containerized applications. Every cluster has at least one worker node.
• Nodes: A worker machine in a Kubernetes cluster, which can be either a virtual or a physical machine. Each node is managed by the control plane and contains the services necessary to run Pods.
• Control Plane: The set of components that manages the overall state of the cluster. It makes global decisions about the cluster (e.g., scheduling) and detects and responds to cluster events.
• Pod: The smallest and simplest unit in the Kubernetes object model that you create or deploy. A Pod represents a single instance of a running process in your cluster and can contain one or more containers.
• Deployment: A resource object that provides declarative updates for Pods and ReplicaSets. You describe a desired state in a Deployment, and the Deployment Controller changes the actual state to the desired state at a controlled rate.
• Service: An abstract way to expose an application running on a set of Pods as a network service. Services provide a stable endpoint (IP address and DNS name) for a group of Pods.
• Namespace: A way to divide cluster resources between multiple users or teams. This concept is similar to Kestra’s namespaces, which help organize and secure workflows.

How does Kubernetes work?

Kubernetes operates on a client-server architecture, with a central control plane (often called the master) managing a fleet of worker nodes. This distributed design ensures high availability and scalability.

Understanding Kubernetes architecture: clusters, nodes, and pods

A Kubernetes cluster is composed of two main types of components: the control plane and the worker nodes.

The Control Plane components include:

• kube-apiserver: The front end for the Kubernetes control plane. It exposes the Kubernetes API, which is how users, management devices, and other cluster components communicate.
• etcd: A consistent and highly-available key-value store used as Kubernetes’ backing store for all cluster data.
• kube-scheduler: Watches for newly created Pods that have no node assigned and selects a node for them to run on based on resource requirements and other constraints.
• kube-controller-manager: Runs controller processes. These controllers watch the state of the cluster through the API server and make changes to move the current state towards the desired state.

Worker Node components include:

• kubelet: An agent that runs on each node in the cluster. It ensures that containers are running in a Pod.
• kube-proxy: A network proxy that runs on each node, maintaining network rules and enabling communication to your Pods from network sessions inside or outside of your cluster.
• Container Runtime: The software responsible for running containers, such as containerd or Docker.

Container orchestration with Kubernetes

Container orchestration is the automated process of managing the lifecycle of containers. Kubernetes excels at this by providing powerful automation features:

• Scheduling: Automatically places containers on nodes based on their resource needs and other constraints, while not sacrificing availability.
• Self-healing: Restarts containers that fail, replaces and reschedules containers when nodes die, and kills containers that don’t respond to user-defined health checks.
• Load Balancing: If traffic to a container is high, Kubernetes can load balance and distribute the network traffic to ensure the deployment is stable.
• Automated Rollouts and Rollbacks: You can progressively roll out changes to your application or its configuration, while monitoring application health to ensure it doesn’t kill all your instances at the same time. If something goes wrong, Kubernetes will roll back the change for you.

Deployment, scaling, and management of applications

Managing applications in Kubernetes is done declaratively. You create manifest files (typically in YAML) that describe your application’s components—Deployments, Services, ConfigMaps, etc. This declarative approach is central to GitOps principles, where the desired state of the system is version-controlled in Git.

• Deployments and ReplicaSets: A Deployment ensures that a specified number of pod “replicas” are running at any given time. A ReplicaSet, managed by the Deployment, is responsible for maintaining this stable set of replica Pods.
• Scaling: The Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a replication controller, deployment, or replica set based on observed CPU utilization or other custom metrics.
• Stateful Applications: For applications that require stable network identifiers and persistent storage, Kubernetes provides StatefulSets.
• Node-level workloads: For tasks that need to run on every node in the cluster, such as log collectors or monitoring agents, DaemonSets are used.

Kubernetes vs. other container technologies

The container ecosystem is vast, and it’s important to understand where Kubernetes fits in, especially in relation to Docker.

Is Kubernetes the same as Docker?

No, Kubernetes and Docker are not the same, but they work very well together. Docker is a platform for creating, shipping, and running individual containers. It provides the tooling to build a container image and a runtime to execute it.

Kubernetes, on the other hand, is an orchestrator for containers. It doesn’t build or run containers itself; it manages them at scale across a cluster of machines. You can think of it this way: Docker provides the car (the container), while Kubernetes provides the traffic control system, highways, and mechanics to manage a whole fleet of cars. Most Kubernetes deployments use Docker (or more accurately, containerd, which originated from Docker) as the underlying container runtime. Kestra’s own Docker plugin allows for direct interaction with the Docker engine for building and running containers within a workflow.

Kubernetes and container runtimes

Kubernetes is designed to be pluggable and supports various container runtimes through the Container Runtime Interface (CRI). While Docker was the original runtime, the ecosystem has evolved. Today, containerd is the most common runtime used in Kubernetes clusters. Others like CRI-O are also popular. This flexibility allows Kubernetes to remain independent of any single containerization technology.

When to use Kubernetes vs. alternative solutions

Kubernetes is powerful, but its complexity means it’s not always the right tool for every job.

• Single Host / Local Development: For running a few containers on a single machine, Docker Compose is often a simpler and more efficient choice.
• Simple Multi-Host Orchestration: Docker Swarm offers a less complex alternative to Kubernetes for basic container orchestration across multiple hosts.
• Serverless Containers: For workloads where you don’t want to manage the underlying cluster infrastructure, services like AWS Fargate, Google Cloud Run, or Azure Container Instances are excellent options. They allow you to run containers without provisioning or managing servers.

The decision often comes down to scale and complexity. If you are managing a microservices architecture with dozens or hundreds of services that need to scale independently and communicate reliably, Kubernetes is the industry standard. For simpler applications, the overhead of managing a Kubernetes cluster might not be justified. Kestra’s architecture reflects this flexibility, offering various task runner types that can execute tasks on Docker, in local processes, or as Kubernetes pods.

Benefits of using Kubernetes

Organizations adopt Kubernetes for a range of strategic advantages that go beyond simple container management. It provides a common platform that bridges development and operations, accelerating software delivery and improving system reliability.

Why organizations adopt Kubernetes

The primary driver for Kubernetes adoption is the need to manage complexity at scale. As companies move to microservices architectures, the number of deployable components explodes. Kubernetes provides a unified API and a consistent operational model to manage this complexity, leading to:

• Increased Agility: Teams can deploy applications faster and more frequently.
• Improved Reliability: Self-healing and automated rollouts reduce downtime.
• Better Resource Utilization: Kubernetes efficiently packs containers onto nodes, optimizing infrastructure costs.
• Developer Productivity: A consistent platform across development, testing, and production environments streamlines the developer workflow.

Scalability and high availability

Kubernetes is designed from the ground up for scale and resilience. The Horizontal Pod Autoscaler can automatically increase or decrease the number of application instances based on real-time metrics like CPU or memory usage. This ensures that applications can handle traffic spikes without manual intervention.

High availability is achieved through redundancy. By running multiple instances of application pods across different nodes, Kubernetes can tolerate node failures. If a node goes down, the pods running on it are automatically rescheduled to healthy nodes, ensuring continuous service availability.

Portability across different environments

One of the most significant benefits of Kubernetes is its portability. It provides a consistent abstraction layer over the underlying infrastructure, whether it’s on-premises data centers, public clouds, or a hybrid combination. This allows organizations to build applications that can run anywhere without modification. This is a key enabler for multi-cloud orchestration and hybrid cloud automation, preventing vendor lock-in and giving businesses the flexibility to choose the best infrastructure for their needs.

Real-world applications and use cases

Kubernetes is used by thousands of organizations, from startups to large enterprises, to run their most critical applications. For example, Apple’s ML team uses Kestra to orchestrate large-scale data pipelines, which often run in containerized environments managed by systems like Kubernetes to handle the massive scale. Leading enterprises in finance and resources, such as JPMorgan Chase and BHP, leverage Kestra for complex infrastructure and cybersecurity workflows, which frequently involve orchestrating tasks across Kubernetes clusters and other systems.

Is Netflix using Kubernetes?

This is a common question, given Netflix’s influence on cloud-native architecture. While Netflix was a pioneer in microservices and containerization, they developed their own in-house container orchestration platform called Titus. Titus serves a similar purpose to Kubernetes but is deeply integrated with the AWS ecosystem and tailored to Netflix’s specific scale and requirements. So, while they don’t use Kubernetes directly for their core streaming services, their work on Titus has contributed valuable insights to the broader container orchestration community.

How Kestra approaches Kubernetes orchestration

While Kubernetes is a powerful platform for running applications, orchestrating the complex, multi-step, polyglot workflows around those applications often requires a higher-level control plane. This is where Kestra excels. Kestra acts as a universal orchestration layer that can manage not only Kubernetes resources but also the data, infrastructure, and business processes that interact with them.

Kestra simplifies Kubernetes operations by allowing you to define and manage complex workflows using declarative YAML. Instead of writing brittle shell scripts or custom operators, you can use Kestra’s rich set of plugins and flow-control logic to automate tasks like:

• Deploying applications using the kubectl plugin.
• Running long-running, resource-intensive tasks as isolated pods.
• Automating the entire lifecycle of a pod, from creation to cleanup.
• Implementing AI-powered incident monitoring for your cluster.

A key feature is the Kubernetes Task Runner, which allows any Kestra task—be it Python, SQL, or a shell script—to be executed as a dedicated Kubernetes pod. This provides ultimate isolation, resource management, and scalability for your workflows.

Furthermore, the Kestra Kubernetes Operator brings GitOps to your workflows. You can manage your Kestra flows, templates, and namespace files as Kubernetes Custom Resources, allowing you to version-control your entire orchestration layer alongside your application and infrastructure code.

Here is a simple example of a Kestra flow that uses the PodCreate task from the Kubernetes plugin to launch an Nginx pod:

id: kubernetes-pod-create-example
namespace: company.team

tasks:
  - id: create-nginx-pod
    type: io.kestra.plugin.kubernetes.core.PodCreate
    namespace: default
    spec:
      containers:
        - name: nginx
          image: nginx:1.25.3
          ports:
            - containerPort: 80

This declarative workflow is easy to read, version, and reuse. By combining Kestra and Kubernetes, you can simplify provisioning and deployment and build a robust, auditable control plane for all your infrastructure automation needs. You can explore more patterns in our dedicated infrastructure resources.

Learning Kubernetes and future relevance

As Kubernetes has become a foundational technology for cloud-native computing, understanding its concepts and future direction is crucial for developers and operations professionals.

Is Kubernetes easy to learn?

Kubernetes has a reputation for being complex, and its learning curve can be steep. The platform encompasses a wide range of concepts, from networking and storage to security and scheduling. However, its modular design allows you to learn it incrementally. Starting with core concepts like Pods, Deployments, and Services, and then gradually exploring more advanced features, makes the learning process manageable. Abundant documentation, tutorials, and a vibrant community provide extensive support for newcomers.

Can I learn Kubernetes in 2 days?

While it’s possible to grasp the basic concepts and deploy a simple application within a couple of days, achieving proficiency with Kubernetes takes longer. A two-day bootcamp can provide a strong conceptual foundation, but true mastery comes from sustained, hands-on experience: deploying real applications, troubleshooting issues, and managing clusters in production-like environments.

Is Kubernetes still relevant in 2026?

Absolutely. In 2026, Kubernetes remains the de-facto standard for container orchestration. Its adoption is widespread across all major cloud providers and a vast number of enterprises. The ecosystem around Kubernetes continues to grow, with new tools and platforms being built on top of it. While other technologies like serverless and WebAssembly are emerging, they often complement Kubernetes rather than replace it. Kubernetes is a mature, stable, and evolving platform that will continue to be a critical part of the cloud-native landscape for the foreseeable future.

Why are people moving away from Kubernetes?

While Kubernetes adoption is still growing, some teams and organizations are exploring alternatives for specific use cases. The primary reasons for this shift are complexity and operational overhead. Managing a Kubernetes cluster requires specialized expertise and can be resource-intensive, especially for smaller teams or simpler applications. In these scenarios, managed container platforms (like AWS Fargate), Platform-as-a-Service (PaaS) offerings, or even simpler orchestrators may provide a better trade-off between control and convenience. The trend is not a mass exodus from Kubernetes but rather a maturation of the market, where organizations choose the right tool for the job.

Getting started with Kubernetes

Starting your journey with Kubernetes involves setting up a local environment and learning the basic commands to interact with a cluster.

Essential tools and prerequisites

Before you begin, you’ll need a few key tools:

• A container runtime: Docker Desktop is a popular choice as it includes Docker, containerd, and a single-node Kubernetes cluster.
• kubectl: The command-line tool for interacting with the Kubernetes API. You use it to deploy applications, inspect and manage cluster resources, and view logs.
• A local Kubernetes cluster: Tools like minikube or kind allow you to run a lightweight, single-node Kubernetes cluster on your local machine for development and testing.

Setting up your first Kubernetes cluster

For local development, minikube start or kind create cluster are the quickest ways to get a cluster running. These tools create a virtual machine or Docker container on your laptop that acts as a Kubernetes node.

For more production-like environments, all major cloud providers offer managed Kubernetes services:

• Amazon Elastic Kubernetes Service (EKS)
• Google Kubernetes Engine (GKE)
• Azure Kubernetes Service (AKS)

These services handle the complexity of managing the control plane, allowing you to focus on your applications. You can deploy Kestra on any of these Kubernetes distributions using our official Helm chart.

Deploying an application on Kubernetes

Deploying an application typically involves writing a YAML manifest file. Here’s a minimal example for a Deployment that runs two Nginx replicas:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.25.3
        ports:
        - containerPort: 80

You would save this as deployment.yaml and apply it to your cluster with the command: kubectl apply -f deployment.yaml. Kubernetes will then create the pods and ensure two instances are always running.

Advanced Kubernetes concepts

Once you’ve mastered the basics, Kubernetes offers a rich set of advanced features for managing complex applications, enhancing security, and improving observability.

Service meshes and networking

As microservice architectures grow, managing communication between services becomes complex. A service mesh, such as Istio or Linkerd, is an infrastructure layer that handles service-to-service communication. It provides features like traffic management, security (mTLS), and observability (metrics, tracing) without requiring changes to the application code. Kubernetes also has powerful built-in networking concepts like NetworkPolicies for controlling traffic flow between pods and Ingress for managing external access to services.

Security best practices in Kubernetes

Securing a Kubernetes cluster is a multi-faceted task. Key best practices include:

• Role-Based Access Control (RBAC): Use RBAC to define granular permissions for users and services interacting with the Kubernetes API.
• Pod Security Standards: Enforce security policies on pods to restrict their capabilities, such as preventing them from running as root.
• Network Segmentation: Use NetworkPolicies to isolate workloads and limit the blast radius of a potential compromise.
• Secret Management: Avoid storing secrets directly in manifests. Use Kubernetes Secrets, or integrate with an external secrets manager like HashiCorp Vault. Kestra’s own secret management capabilities can be used to securely inject credentials into workflows that interact with Kubernetes.

Monitoring and logging solutions

Effective monitoring and logging are critical for operating applications on Kubernetes. The de-facto standard for monitoring is Prometheus, an open-source tool that scrapes metrics from cluster components and applications, paired with Grafana for visualization. For logging, a common pattern is to use a combination of Fluentd to collect logs from nodes and ship them to a centralized backend like Elasticsearch or Loki. Adopting a standardized observability framework like OpenTelemetry can provide a unified way to collect traces, metrics, and logs from your applications. You can also explore Kestra’s guides on monitoring and alerting for your orchestration platform.