
Master Kubernetes fundamentals from installation and configuration to auto scaling, auto healing, and load balancing; deploy apps, monitor logs, and explore Docker containers with public and private cloud deployments.
Explore how Kubernetes, an open source framework, automates deployment and manages containerized, cluster-wide applications, enabling reliable, scalable operations across infrastructures.
Discover why Kubernetes matters for container orchestration, auto scaling, and production deployment in the cloud, and see which major companies use Kubernetes with clusters for high availability.
Discover why Kubernetes matters by integrating infrastructure as a service and platform as a service from cloud providers like AWS, Microsoft, and Google Cloud to deploy containers in a cluster.
Meet AJ, an experienced instructor with deep expertise in cloud deployment infrastructure and Linux. He shares practical insights from infrastructure deployment projects and offers training for this course.
Explore how Docker containers package applications with their dependencies into lightweight isolated images on a shared Linux kernel, enabling portable, reproducible environments and easy cloud deployment.
Compare containers with virtual machines to reveal performance and isolation advantages of the Docker engine. Emphasize lightweight, fast boot, portability, and efficient resource use over bare metal.
Explore Docker architecture, where a client uses commands like docker build and docker run to create images and containers via the docker daemon, pulling from the registry or docker hub.
Discover Docker Hub, a cloud registry for official and custom images with private or public repositories, free to use, and seamless pull and push for CI/CD deployments.
Explore Docker Hub in this hands-on session, browse official images, log in, and learn to use these images with revision control tool and cloud for tutorials and deployments.
Explore Kubernetes architecture, detailing the control plane, etcd master capabilities, and how worker nodes, pods, and containers, often running Docker, operate across a cluster.
Explore kubernetes architecture, including master and worker nodes, api server, etcd, scheduler, controller-manager, kubelet, and kube-proxy, and how they manage replication, autoscaling, load balancing, rolling update, service discovery, and authentication.
Learn essential Kubernetes terminology, including clusters, nodes, labels, and pods, and discover how to create pods with one or more containers and set environment keys and values.
Learn how to write YAML configuration files to deploy apps in a Kubernetes cluster, including defining spec, image, ports, labels, and avoiding whitespace and tab errors.
Discover how a pod is the atomic unit running one or more containers, sharing memory and networking, with deployments and services enabling scaling, healing, and stable networking.
Master kubeadm and kubectl to build and manage Kubernetes clusters on own infrastructure, deploy resources, and join nodes across non-cloud environments.
Install and configure a Kubernetes cluster on Vagrant virtual machines. Build a master and two workers on our own infrastructure, and explore on-premises deployment versus cloud options.
Spin up a cluster of vagrant VMs for a hands-on Kubernetes and Docker course by provisioning an image and configuring grid-based setup.
Deploy a three-node Kubernetes cluster by using ansible to install docker, kubeadm, kubectl, and CNI on one master and two workers, configured via an inventory and a playbook.
This lecture covers installing and configuring the Kubernetes master node, disabling swap, initializing the cluster, deploying the network plugin, and joining workers to form a ready cluster.
Install and configure minion worker nodes for a Kubernetes cluster by turning swap off, installing prerequisites, joining the cluster, and verifying connectivity to the api server on port 6443.
Explore your Kubernetes cluster with kubectl to check node status, namespaces, and pods, view deployments, and troubleshoot resource issues while deploying a MySQL database using Docker.
Master Docker fundamentals: the Docker daemon, client-server model, and CLI for creating, running, and committing containers from images, with data mapping and container networking.
Docker image registry stores images, supports public and private access, and enables pull and push workflows for development, testing, and production.
Learn to create and customize a docker image from a base image, write a Dockerfile, build and run a container, expose ports, and validate the image by testing the server.
Push a Docker image to a registry via the command line, including signing up, logging in, listing images, tagging, and uploading from local to the registry.
Learn to pull a Docker image from the command line, list local images, and remove images—forcefully if needed—before using the image to run a container.
Run containers with docker by creating and starting them, access them with docker exec, and learn how images from local systems or the docker registry are used to create containers.
learn how to map container ports to the host to expose apps outside the container, using publish and expose options, and access them via the host IP and mapped port.
Learn to install docker on a 64-bit system, pull images from a repository, create and run containers, and verify installations with basic image management.
Create and run a container from a prepared image, verify installed packages, commit changes to a new image, and push it to a registry while preserving container state.
Create a custom image from a dockerfile by loading a unix base image, copying files, and exposing ports, then build, run, and map ports to verify the service.
Discover Google Kubernetes Engine basics, set up a Google Cloud account with two-factor authentication, configure billing, create a project, and deploy clusters and workloads.
Create a GKE cluster, name it, select a zone and machine type, enable container-optimized OS, and deploy a three-node cluster with a master and two workers managed by the API.
Connect to a Google Kubernetes Engine cluster from the command line by configuring kubectl access with gcloud, verify nodes, and prepare the environment to deploy applications.
Determine GKE allocatable resources by accounting for memory and storage reserved for internal operations. See how instance type and resource reservations affect available memory, with cloud shell commands to verify.
Learn to manage Kubernetes objects with a mix of declarative yaml configurations and imperative commands. Use kubectl apply to keep changes trackable and reproducible.
Explore namespace in Kubernetes to organize resources across multiple projects within a cluster, using default and system namespaces, and assign isolation for apps and services.
Understand how namespaces partition a Kubernetes cluster, isolating most resources while storage remains global. Use kubectl --namespace or set-context to work inside a namespace, noting name uniqueness within that namespace.
Build a container image from the sample application and deploy it on Google Kubernetes Engine, exposing the service and validating the cluster deployment.
Learn to expose a Kubernetes deployment to the internet using a load balancer, map port 80, and obtain a public IP so the app is accessible from anywhere.
Scale deployments by adjusting replicas with kubectl, demonstrating scale up from one to three and scale down from three to one, and monitor status with get deployments.
Create a pod using a configuration file in a Kubernetes and Docker hands-on course, using kubectl to deploy nginx and troubleshoot configuration issues.
Explore how labels assign key-value pairs to Kubernetes objects for meaningful organization, filtering, and multi-cluster management across production sites and dashboards.
Explore how Kubernetes labels work as key-value identifiers and how label selectors, including equality-based and set-based types, filter pods and objects via yaml configurations.
Define image pull policy for containers and its default behavior of pulling from the internet when not locally present. Avoid deprecated features and prefer local image caching to improve reliability.
Learn how to create and manage Kubernetes deployments, roll out updates, monitor rollout status and history, perform rollbacks, and update container images for scalable applications.
Learn how to manage storage in Kubernetes pods using volumes and persistent disks, ensuring data persists beyond pod restarts, with yaml configuration and Google Cloud integration.
Learn to create and use Google Cloud persistent storage with Kubernetes, including creating a 500 GB persistent disk via YAML, formatting, mounting, and preserving data across pod restarts.
Discover how to deploy apps with dynamic persistent storage by using storage classes and persistent volume claims, enabling automatic provisioning and flexible access modes for multiple applications.
Create a storage class using yaml, then provision a persistent volume claim and assign it to your application, choosing between standard and ssd based on cost and performance.
Learn to dynamically provision persistent storage in Kubernetes with a storage class and PVC, enabling a stateful app with ReadWriteOnce access and backend-agnostic storage.
Deploy a kubernetes application using dynamic persistent storage by configuring a deployment with labels, a single replica, and pvc mounted to the container path, port 1880; then apply and verify.
Master deploying a Jenkins-like app on Kubernetes with dynamic persistent storage, using storage class and a persistent volume claim, and exposing it via a load balancer with a public IP.
This Kubernetes Docker Complete Course will help you to understand how to run, deploy and maintain your applications on Kubernetes. If you are into DevOps, then this is a technology you need to learn. Kubernetes has gained a lot of popularity lately and it is a well-aimed skill by companies.
The course will show you how to build apps in containers using docker and how to deploy it Kubernetes cluster. On the other hand,this Kubernetes Course will explain you how to set up your Kubernetes cluster on your desktop, or on the cloud using AWS.
What is Kubernetes?
Kubernetes is an open-source container orchestration technology system .Kubernetes is mainly used for automating deployment, scaling and management of containerized applications.
Kubernetes was originally designed by Google and then open sourced.And from then to now Kubernetes is maintained by the Cloud Native Computing Foundation.
Kubernetes aims to provide a "platform for automating deployment, scaling, and operations of application containers across clusters of hosts It works with a range of container tools, including Docker.
Kubernetes is a portable as well as extensible platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. Kubernetes has a large, rapidly growing ecosystem. There are widely availablity in Kubernetes services, support, and tools.
Google open-sourced the Kubernetes project in 2014. Kubernetes builds upon a decade and a half of experience that Google has with running production workloads at scale, combined with best-of-breed ideas and practices from the community.
Even though Kubernetes provides a lot of functionality, there are always new scenarios that would benefit from new features. Application-specific workflows can be streamlined to accelerate developer velocity. Ad hoc orchestration that is acceptable initially often requires robust automation at scale. This is why Kubernetes was also designed to serve as a platform for building an ecosystem of components and tools to make it easier to deploy, scale, and manage applications.
Labels in Kubernetes empower users to organize their resources however they please. Annotations enable users to decorate resources with custom information to facilitate their workflows and provide an easy way for management tools to checkpoint state.
The Kubernetes control plane is mainly built upon the same APIs that are available to developers and users. Users can write their own controllers, such as schedulers, with their own APIs that can be targeted by a general-purpose command-line tool.
This design has enabled a number of other systems to build the Kubernetes.
On the other hand Kubernetes is not a traditional or all-inclusive Platform as a Service( PaaS) system. Since Kubernetes operates at the container level rather than at the hardware level, it provides some generally applicable features common to PaaS offerings, such as deployment, scaling, load balancing, logging, and monitoring. However, Kubernetes is not monolithic, and these default solutions are optional and pluggable. Kubernetes provides the building blocks for building developer platforms but preserves user choice and flexibility where it is important.
Here are some advantages of Kubernetes
Firstly Kubernetes does not limit the types of applications supported. Moreover, Kubernetes aims to support an extremely diverse variety of workloads, including stateless and stateful even data-processing workloads. Thatswhy If an application can run in a container, it should run great on Kubernetes.
Kubernetes never deploy source code and does not build your application. Continuous Integration, Delivery, and Deployment (CI/CD) workflows are determined by organization cultures and preferences as well as technical requirements.
Kubernetes does not provide application-level services, such as middleware (e.g., message buses), data-processing frameworks (for example, Spark), databases like MySQL, caches as built-in services. Such components can run on Kubernetes, and/or can be accessed by applications running on Kubernetes through portable mechanisms, such as the Open Service Broker.
Kubernetes never dictate logging, monitoring, or alerting solutions. It provides some integrations as proof of concept, and mechanisms to collect and export metrics.
Kubernetes does not also provide nor mandate a configuration language/system (e.g., jsonnet). It provides a declarative API that may be targeted by arbitrary forms of declarative specifications.
Kubernetes never provide nor adopt any comprehensive machine configuration, maintenance, management, or self-healing systems.
Though Kubernetes is not a mere orchestration system. In fact,kubernetes eliminates the need for orchestration. In contrast, Kubernetes is comprised of a set of independent, composable control processes.Kubernetes also drive the current state continuously towards the provided desired state.
What is Kubernetes for ?
Kubernetes is an open source system for managing containerized applications across multiple hosts, providing basic mechanisms for deployment, maintenance, and scaling of applications.In short, Kubernetes is a container orchestration tool from Google.
How Does Kubernetes work?
Kubernetes can mainly defines as a set of building blocks.Kubernetes collectively provides mechanisms that deploy, maintain and scale applications. Kubernetes is loosely coupled and extensible to meet different workloads. This extensibility is provided in large part by the Kubernetes API, which is used by internal components as well as extensions and containers that run on Kubernetes
There are some key components which are widely used is Kuberntes are
Pods
Labels and selectors
Controllers
Services
What is the pod in Kubernetes
A Kubernetes pod is a group of containers. Pods can be deployed together on the same host. If you frequently deploy single containers, you can generally replace the word "pod" with "container" and accurately understand the concept.
Pods operate at one level higher than individual containers because it's very common to have a group of containers work together to produce an artifact or process a set of work.
For example, consider this pair of containers: a caching server and a cache "warmer". You could build these two functions into a single container, but now they can each be tailored to the specific task and shared between different projects.
What is labels and selectors in Kubernetes?
Labels and selectors are the primary grouping mechanism in Kubernetes and determine the components an operation applies to.
Labels are key-value pairs that are attached to Kubernetes objects, such as Pods. They are intended to be used to specify object attributes which are meaningful and relevant to users, while not directly impacting implying semantics of the core system.
Unlike names and UIDs, labels in Kubernetes do not provide uniqueness. In general, we expect many objects to carry the same label(s).
Via a label selector, the client/user can identify a set of objects. The label selector is the core grouping primitive in Kubernetes.
The API currently supports two types of selectors: equality-based and set-based. A label selector can be made of multiple requirements which are comma-separated. In the case of multiple requirements, all must be satisfied so the comma separator acts as a logical AND (&&) operator.
What is Controllers in Kubernetes?
A controller is a reconciliation loop that drives actual cluster state toward the desired cluster state. It does this by managing a set of pods. One kind of controller is a replication controller, which handles replication and scaling by running a specified number of copies of a pod across the cluster. It also handles creating replacement pods if the underlying node fails. Other controllers that are part of the core Kubernetes system include a "DaemonSet Controller" for running exactly one pod on every machine or some subset of machines and a "Job Controller" for running pods that run to completion, as part of a batch job. The set of pods that a controller manages is determined by Kubernetes label selectors that are part of the controller’s definition.
What is Services in Kubernetes?
A Kubernetes service is a set of pods which can work together, such as one tier of a multi-tier application. The set of pods that constitute a service are defined by a label selector.Kubernetes provides service discovery and request routing by assigning a stable IP address and DNS name to the service, and load balances traffic in a round-robin manner to network connections of that IP address among the pods matching the selector even as failures cause the pods to move from machine to machine.By default, a service is exposed inside a cluster but a service can also be exposed outside a kubernetes cluster.
Why you should start learning Kubernetes?
When our clients ask us to create a highly available distributed system, we usually recommend Kubernetes, because Kubernetes is really easy to use and even though it has a steep initial learning curve and kubernetes still a lot more simple than other competing orchestration engines.
Kubernetes also works perfectly with Node apps because it's based on Docker.Thatswhy you can run any kind of application using Kubernetes.
As we all know kubernetes is a open source project spun out of Google which automates the process of deploying and managing multi-container applications at scale. While Kubernetes works mainly with Docker, it can also work with any container system that conforms to the Open Container Initiative (OCI) standards for container image formats and runtimes.
And because Kubernetes is open source, with relatively few restrictions on how it can be used, it can be used freely by anyone who wants to run containers, most anywhere they want to run them.
Kubernetes introduces new abstractions and concepts, and the learning curve for Kubernetes is high so it is only normal to ask what the long-term payoffs are for using Kubernetes. Here’s a rundown of some of the specific ways running apps inside Kubernetes becomes easier.
Kubernetes manages app health, replication, load balancing, and hardware resource allocation for you. One of the most basic duties Kubernetes takes off your hands is the busywork of keeping an application up, running, and responsive to user demands. Apps that become “unhealthy,” or don’t conform to the definition of health you describe for them, can be automatically healed using kubernetes.
Another benefit of Kubernetes is that it maximizes the use of hardware resources including memory, storage I/O, and network bandwidth. Applications can have soft and hard limits set on their resource usage. Many apps that use minimal resources can be packed together on the same hardware; apps that need to stretch out can be placed on systems. And again, rolling out updates across a cluster, or rolling back if updates break, can be automated.
Kubernetes Helm charts ease the deployment of preconfigured applications. Package managers such as Debian Linux’s APT and Python’s Pip save users the trouble of manually installing and configuring an application. This is especially handy when an application has multiple external dependencies.
Helm is something like a package manager for Kubernetes. Many popular software applications must run as multiple, ganged-together containers in Kubernetes. Helm provides a definition mechanism, a “chart,” that describes how a given piece of software can be run as a group of containers inside Kubernetes.
Kubernetes simplifies management of storage, secrets, and other application-related resources. Containers are meant to be immutable; whatever you put into them isn’t supposed to change. But applications need state so it means they need a reliable way to deal with external storage volumes. That’s made all the more complicated by the way containers live, die, and are reborn across the lifetime of an app.
Kubernetes always allow containers and apps to deal with storage in the same decoupled way as other resources. Many common kinds of storage, from Amazon EBS volumes to plain old NFS shares, can be accessed via Kubernetes storage drivers, called volumes. Normally, volumes are bound to a specific pod, but a volume subtype called a “Persistent Volume” can be used for data which needs to live on independently of any pod in Kubernetes.
Sometimes these containers need to work with “secrets” which is nothing but credentials like API keys or service passwords that you don’t want hardwired in a container or stashed openly on a disk volume. While third-party solutions are available for this, like Docker secrets and HashiCorp Vault,
In that case, Kubernetes uses its own mechanism of handling secrets. For instance, Etcd must be configured to use SSL/TLS when sending information including secrets between nodes, rather than in plaintext.
Kubernetes apps can run in hybrid and multi-cloud environments. One of the long-standing dreams of cloud computing is to be able to run any app in any cloud or any mix of clouds public or private.
Kubernetes’s architecture makes use of various concepts and abstractions. Some of these are variations on existing, familiar notions, but others are specific to Kubernetes.
The highest-level Kubernetes abstraction, the cluster, refers to the group of machines running Kubernetes (itself a clustered application) and the containers managed by it. A Kubernetes cluster must have a master, the system that commands and controls all the other Kubernetes machines in the cluster. A highly available Kubernetes cluster replicates the master’s facilities across multiple machines. But only one master at a time runs the job scheduler and controller-manager.
Each cluster contains Kubernetes nodes. This Kubernetes nodes might be physical machines or VMs. Again, the idea is abstraction: whatever the app is running on, Kubernetes handles deployment on that substrate. It is also possible to ensure that certain containers run only on VMs or only on bare metal.
Nodes run pods, the most basic Kubernetes objects that can be created or managed. Each pod represents a single instance of an application or running process in Kubernetes and consists of one or more containers. On the other hand Kubernetes starts, stops, and replicates all containers in a pod as a group. Pods keep the user’s attention on the application, rather than on the containers themselves.
Pods are created and destroyed on nodes as needed to conform to the desired state specified by the user in the pod definition. Kubernetes provides an abstraction called a controller for dealing with the logistics of how pods are spun up, rolled out, and spun down.
Controllers come in a few different flavors depending on the kind of application being managed. For instance, the recently introduced “StatefulSet” controller is used to deal with applications that need persistent state. Another kind of controller, the deployment, is used to scale an app up or down, update an app to a new version, or roll back an app to a known-good version if there’s a problem.
Because pods in Kubernetes live and die as needed, we need a different abstraction for dealing with the application lifecycle. An application is supposed to be a persistent entity, even when the pods running the containers that comprise the application aren’t themselves persistent.
To that end, Kubernetes provides an abstraction called a service.
A service describes how a given group of pods or other Kubernetes objects can be accessed via the network. As the Kubernetes documentation puts it, the pods that constitute the back end of an application might change, but the front end shouldn’t have to know about that or track it. Services make this possible.
A few more pieces internal to Kubernetes round out the picture. The scheduler parcels out workloads to nodes so that they’re balanced across resources and so that deployments meet the requirements of the application definitions. The controller manager ensures the state of the system—applications, workloads, etc.—matches the desired state defined in Etcd’s configuration settings.
How much does a Kubernetes Engineer can make per annum?
The salaries of kubernetes engineers are also very attractive.
Software Engineer- $76K to $152K
Senior Software Engineer - $103K to $166K
Development Operations (DevOps) Engineer - $84K to $154K
Site Reliability Engineer (SRE) - $69K to $164K
Sr. Software Engineer / Developer / Programmer - $92K to $157K
Software Development Engineer - $104K to $118K
Software Architect - $95K to $154K
Career Opportunities in Kubernetes and Companies using Kubernetes :
In Kubernetes there are no. of jobs worldwide and the companies which are hiring Kubernetes experts some are listed below
Red Hat
Oracle
Microsoft
Samsung SDS
IBM
ING
Huawei
Naic
Monjo
Philips
SAP
Soundcloud
Yahoo
Wink
Sling
OpenAI
The New York Time
Mirantis
Centurylink
Apprenda
GitHub
Weaveworks
Asana
Buffer
CircleCi
Evernote
Harvest
Intel
InVisionApp
Shopify
Starbucks
Digital Ocean
Capscale
Adform
Amadeus
Ancestry
Box
BlackRock
Comcast
eBay and many more
30-day money-back guarantee!
Now again coming to this course,So this Kubernetes course is ideal for beginners to experts to container technology or those who are new to Kubernetes engineering or who want to enrich their knowledge in Kubernetes much more.
This course includes the following chapters:
Course Introduction and Overview
What is Kubernetes?
Why Kubernetes?
Up and Running: Basics
Cluster Architecture
Core Objects and Primitives
The introduction lectures that show you the Kubernetes desktop installation are free to preview, so you can already put a glance over it before buying the course!
So what are you waiting for? Enroll in the course and get started with Kubernetes today!
You will get 30-day money-back guarantee from Udemy for this course.
If not satisfied simply ask for a refund within 30 days. You will get full refund. No questions whatsoever asked.
Are you ready to take your DevOps skills and career to the next level, take this course now!
You will go from zero to Kubernetes hero in few hours.