In the IT industry, everything is constantly changing. The needs of businesses are evolving. User demands are constantly changing and evolving. The infrastructure is also rapidly developing. And it is so because IT views transformation as the exception rather than the rule. Even so, the only constant in the world is changing.
The value that the container image gave drove the rising usage of containers in recent years. The availability of a deployable artifact (the docker image) that grouped all requirements, from the operating system to middleware and application components, enabled substantial gains in development and operational (DevOps) efficiency. And the ease with which containers might be deployed aided in the expansion and refinement of approaches centered on infrastructure as code as well as immutable infrastructure. However, containers alone do not meet the requirement for continual adaptability.
The debut of as a service via AWS and early acceptance of containers has kept almost all of the way IT works fairly untouched. True, the utilization of automation has increased the usage of infrastructure as code, but it has mainly resulted in the automation of traditional practices — script to install the docker runtime on three hosts, to “docker run” 3 distinct microservice images, and a third is to adjust firewall rules that allow traffic through.
This automation still implies a certain amount of reliability; after executing the scripts, things continue to work smoothly. However, if two of the docker hosts go down unexpectedly, the team is back in firefighting mode.
Here comes container orchestration. Kubernetes is the most popular container orchestration solution in the market today, and for a good reason. What distinguishes Kubernetes and other comparable systems is that the system functions in a manner that anticipates continual change.
Adaptability
The Kubernetes paradigm is so powerful because it allows users to declare, “here’s our preferred state.” “We want two instances of the user-facing web page, three instances of the catalog service, and ten instances of the shopping cart service,” and Kubernetes simply makes it happen. It is a logical paradigm that is used to define complicated systems. Kubernetes continually monitors the system’s real state and will repair any deviations from the expected state. So, if we lose the host (Docker) were part of the instances is operating, Kubernetes can instantly resume any required services on the remaining servers, restoring the appropriate state. Change tolerance is integrated into the DNA of Kubernetes.
Kubernetes can be configured to host & manage an almost infinite variety of workloads.
The diversity of an IT team’s infrastructure is another source of pressure. There are several server & storage platforms, as well as an arguably much more diverse range of networking options. And, increasingly, businesses are adopting hybrid, combining “on-premises” or “public cloud” infrastructures. This implies that IT teams must not only become experts in the administration interfaces for several clouds, but the scripts they write to automate the multiplicity of different jobs must also be built and managed for each infrastructure.
Kubernetes solves this by providing abstractions over the various infrastructure assets, enabling Kubernetes users to exploit that infrastructure via common elements such as tasks (pods & replica sets), networks & network rules (Network Policy), plus storage. Kubernetes is built to be infrastructure-agnostic.
Finally, Kubernetes’ extensibility is undoubtedly what excites us most about it. Out of the box, Kubernetes already offers a vast array of resource types—pods, classes, storage, roles, and much more—as well as functionality to maintain those resources throughout their lifecycle—replica sets, stateful sets, daemon sets, and more. However, each stateful workload has specific requirements, such as a cache, database, or indexing service.
In contrast to MySQL, for instance, Mongo DB safeguards the data it saves in a very different way. Kubernetes enables the addition of custom resource definitions (CRDs) and related actions (one of the most common ways to do this is through an operator), hence expanding the platform’s functionality. In other words, Kubernetes may be configured to host and control an almost infinite variety of various kinds of workloads.
In contrast to MySQL, for instance, Mongo DB safeguards the data it saves in a very different way. Kubernetes enables the addition of custom resource definitions (CRDs) and related actions (one of the most common ways to do this is through an operator), hence expanding the platform’s functionality. In other words, Kubernetes may be configured to host and control an almost infinite variety of various kinds of workloads.
What is the purpose of Kubernetes?
Kubernetes, like Docker & server virtualization before it, first grabbed the developer’s mindshare. Having an intelligent, autonomous system that assists them with those daily operations is beneficial to those developers, especially now that they are largely responsible for keeping the software functioning properly in production. App operations include not just the initial deployment activity but also upkeep in the face of infrastructure modifications, security vulnerabilities, and other issues. Kubernetes helps with all of this.
Kubernetes is flexible enough to run software that an organization is not actively creating, such as services that are just in standby mode or software given by an ISV. These services are now mostly operated on virtualized infrastructure, with the assumption of reasonable reliability. Hosting these apps on Kubernetes can instantly improve their robustness & security.
It’s uncommon these days that an organization does not have some, if not significant, container-centric activities underway. It has frequently emerged from a development team that has structured its methods around containers. They’re creating Docker images for their applications, but because the organization lacks a production infrastructure to execute those images, the same app teams manage the container platform. Just as business IT delivers centralized, secure, compliant, and robust virtualized infrastructure settings, the time is now to provide safe, compliant, and robust container platforms. Kubernetes has risen as the obvious market leader in this domain.
Kubernetes Is Becoming Mission Critical
Kubernetes, with its capabilities for running & managing mission-critical workloads, must be as robust to change. If a security issue is discovered that necessitates an update of Kubernetes, it must be fixed immediately, and with little downtime for the operations, it hosts. If app capacity needs unexpectedly increase, Kubernetes capacity must be rapidly increased to meet the demand. When the spike has subsided, Kubernetes must be re-sized to keep IT infrastructure expenses under control.
These are the problems that Kubernetes is striving to solve for containerized workloads. The goal is to oversee Kubernetes using the same approaches and principles that Kubernetes employs for workloads.
Learn Kubernetes online
Get certified in Kubernetes and improve your future career prospects better.
Enroll in Cognixia’s Docker and Kubernetes certification course, upskill yourself and make your way toward success & a better future. Get the best online learning experience with hands-on, live, interactive, instructor-led online sessions with our Kubernetes online training. In this highly competitive world, Cognixia is here to provide you with an immersible learning experience and help you enhance your skillset as well as knowledge with engaging online training that will enable you to add immense value to your organization.
Our Kubernetes online training will cover the basic-to-advanced level concepts of Docker and Kubernetes. This Kubernetes certification course offers you an opportunity to connect with the industry’s expert trainers, develop your competencies to meet industry & organizational standards, and learn about real-world best practices.
This Docker and Kubernetes Certification course will cover the following –
- Essentials of Docker
- Overview of Kubernetes
- Minikube
- Kubernetes Cluster
- Overview Kubernetes Pod
- Kubernetes Client
- Creating and modifying ConfigMaps and Secrets
- Replication Controller and Replica Set
- Deployment
- DaemonSet
- Jobs
- NameSpaces
- Dashboard
- Services
- Exploring the Kubernetes API and Key Metadata
- Managing Specialized Workloads
- Volumes and configuration Data
- Scaling
- RBAC
- Monitoring and logging
- Maintenance and troubleshooting
- The ecosystem
Prerequisites for Docker & Kubernetes Certification
- Basic command knowledge of Linux
- Basic understanding of DevOps
- Basic knowledge of YAML programming language (beneficial, not mandatory)
