Kubernetes Reviews

In my current role, I design and build production data pipelines, maintain and deploy critical infrastructure using Terraform, and manage AWS services such as EC2, RDS, and ELB. I also handle on-call issues as they arise and deploy data bundles to staging, acceptance, and production environments. These tasks are performed on a weekly basis or bi-weekly, depending on the training requirements.

Kubernetes is highly valuable for its node-based setup, which allows for the running of multiple pods. This feature is essential for infrastructure automation and application deployment. 

Kubernetes also offers rollback control and auto-scaling capabilities, which are crucial for maintaining an application's availability even if nodes or pods go down. 

Additionally, Kubernetes supports load balancing to distribute traffic efficiently across multiple pods.

The primary use case for Kubernetes in our environment involves deploying microservices. These microservices are hosted on the Kubernetes platform. We utilize Kubernetes within the insurance sector.

Kubernetes saves time in troubleshooting because it has a self-healing mechanism. If there is an unhealthy cluster or an unhealthy pod, it automatically heals and makes it operational again.

We have clients that require us to deploy their monthly client structures in different locations, with different versions of the application running in various environments. For example, one version might be in one shop, and another version in a different shop. 

We create a container in Docker, and once the container is registered, any changes made can be deployed across all environments, ensuring consistency. Kubernetes handles the deployment process, ensuring everything runs smoothly. The Docker containers perform the necessary tasks for each deployment.

Kubernetes allows you to deploy containers in multiple complex environments. That's one of the main reasons vendors use it. It offers a container infrastructure where you can manage various applications, whether they use MongoDB, SaaS, or other services. Kubernetes acts as a broker to manage everything seamlessly.

Kubernetes improved our deployment and scaling processes. Previously, we used to deploy the application and then restart the server whenever there were changes.

Now, with Kubernetes and Spring Boot, we simply modify the YAML file and deploy it directly. Only the Docker image is different. Even that requires minimal changes.

With proper configurations, you don't need to intervene much. Spring Boot handles a lot for you. This allows you to focus on the end-user experience and scaling usage seamlessly.

We use Kubernetes to manage multiple pods where we store data, topics, and other resources within the Kubernetes cluster. 

We utilize it on Google Cloud Platform for this purpose. It is also used for container orchestration from lower to higher environments due to its ease of use and effective scaling capabilities.

Kubernetes is easy to use, maintain resources, and configure YAML efficiently. Its open-source nature makes it easier to deploy and manage containerized applications across any environment. 

Also, it provides the benefit of an n-plus-one architecture for port scaling.

People are creating a company that is transforming technology and moving from legacy to container technology. So, they are to be able to develop their ability to go to the public cloud easily. They will reroute the application in containers and need to be able to manage it. One of the use cases is becoming cloud-native. They want to be cloud-dependent and cloud-dependent, so you need a new format and containers to move applications easily in any cloud. For this, you need technology to manage multi-cloud applications and balance them.

The tool's most valuable feature is the auto-scaling feature. However, Kubernetes is not used alone; it is part of a complete deployment pipeline integrated with DevOps. This pipeline includes automation and deployment capabilities. 

We use Kubernetes for our data science machine learning platform to orchestrate UPCR Hub containers with UPCR Hub partners. We also use it to orchestrate data pipelines.

Kubernetes allows us to update without downtime and to easily deploy new software, which is very beneficial for our operations.

The solution use case is project-oriented in our organization. Some of our company's clients demand the use of Kubernetes on container technology, while other clients don't. The last time, as part of the company team, I used Kubernetes to create an infrastructure blueprint. My responsibility in our company is to monitor all Kubernetes projects, from DevOps to security operations. 

In our organization, Kubernetes is primarily used to test data environments and develop databases accordingly. The solution is also used in the data migration process. Previously, our company was working on migrating from JSON platforms to the latest Azure cloud platforms using Kubernetes structures. 

The feedback process offered by the solution is crucial. 

For Kubernetes, I'm mainly developing blueprints for both dedicated AKS and Azure AKS. Those are the main use cases. 

Currently, our dedicated AKS Blueprint is the one used in production and is fairly stable. We work on a shared AKS site use case, primarily for cost reduction and maximizing cloud investment.

I'm a vendor and provider for my client. That's my main role under this group. We provide an end-to-end lifecycle, not just spinning things up but also providing other sub-components to complete the building of an AKS product. We can customize it based on client requirements.

One of the most significant improvements we've seen is in the area of dedicated AKS clusters. It's become much more team-efficient because of the use of blueprints. With blueprints, you have everything you need, from IAC infrastructure support to spinning up your AKS deployment to the deployment of Kubernetes operators like search-manager for TLS lifecycle management and other integration operators for products that require them. And it goes beyond that with application deployment as well. 

It's a plug-in type approach, so if I want to integrate a monitoring tool like a data-managed log, I can just set it through and rerun the blueprint. It automatically populates all the necessary parameters and variables before running it. 

Lastly, there are the operational playbooks for things like upgrading your cluster, restarting it, scaling, and patching software. So, basically, think of it as a single unit deployment that contains all the roles you need to perform your AKS lifecycle end-to-end.