Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications.

The abbreviation K8s is derived by replacing the eight letters of “ubernete” with the digit 8. The KubernetesProject was open-sourced by Google in 2014 after using it to run production workloads at scale for more than a decade.


we have to first understand two concepts: immutable infrastructure and containers.

It is a practice where servers, once deployed, are never modified. If something needs to be changed, you never do so directly on the server. Instead, you’ll build a new server from a base image, that have all your needed changes baked in. This way we can simply replace the old server with the new one without any additional modification.

Each container that you run is repeatable; the standardization from having dependencies included means that you get the same behavior wherever you run it.

Containers decouple applications from underlying host infrastructure. This makes deployment easier in different cloud or OS environments.

Features of Kubernetes :

Here are the essential Kubernetes features:

  • Automated Scheduling
  • Self-Healing Capabilities
  • Automated rollouts & rollback
  • Horizontal Scaling & Load Balancing
  • Offers environment consistency for development, testing, and production
  • Infrastructure is loosely coupled to each component can act as a separate unit
  • Provides a higher density of resource utilization
  • Offers enterprise-ready features
  • Application-centric management
  • Auto-scalable infrastructure
  • You can create predictable infrastructure

Master Node

The master node is the first and most vital component which is responsible for the management of Kubernetes cluster. It is the entry point for all kind of administrative tasks. There might be more than one master node in the cluster to check for fault tolerance.

The master node has various components like API Server, Controller Manager, Scheduler, and ETCD. Let see all of them.

API Server: The API server acts as an entry point for all the REST commands used for controlling the cluster.


The scheduler schedules the tasks to the slave node. It stores the resource usage information for every slave node. It is responsible for distributing the workload.

It also helps you to track how the working load is used on cluster nodes. It helps you to place the workload on resources which are available and accept the workload.

K8s can be deployed in very different scenarios depending on the size of the company and its objectives:

  • In-house: Organizations can transform their own data center into a K8s cluster. In this case, companies can take full advantage of their own resources.
  • Cloud: The setup process is similar to an in-house deployment, but includes virtual machines on the cloud. This allows for the creation of a virtually infinite number of machines, depending on demand.
  • Hybrid: An organization’s data center might perform well for most of the day, but sometimes a peak occurs that local computing resources cannot handle. In this case, a hybrid solution works well. When necessary, K8s will create virtual machines on the cloud to better distribute computing resources when on-premise servers are full.
  • On-premise: Some cloud providers have their own K8s implementation embedded. In this case, there is no need to deploy and configure Kubernetes itself; an organization just needs to manage the service. Since deploying Kubernetes can be tricky, this is a good solution for companies that do not have a big IT team capable of handling cluster configuration and maintenance.
  • Multicloud: This is the next level of a hybrid cloud solution. Computing resources are deployed among two or more cloud vendors. In this case, companies need to avoid vendor lock-in and minimize risk if something goes wrong.