#managing Kubernetes clusters

[virtualizationhowto]

Best Kubernetes Management Tools in 2023

Best Kubernetes Management Tools in 2023 #homelab #vmwarecommunities #Kubernetesmanagementtools2023 #bestKubernetescommandlinetools #managingKubernetesclusters #Kubernetesdashboardinterfaces #kubernetesmanagementtools #Kubernetesdashboard

Kubernetes is everywhere these days. It is used in the enterprise and even in many home labs. It’s a skill that’s sought after, especially with today’s push for app modernization. Many tools help you manage things in Kubernetes, like clusters, pods, services, and apps. Here’s my list of the best Kubernetes management tools in 2023. Table of contentsWhat is Kubernetes?Understanding Kubernetes and…

[bdccglobal]

A Kubernetes CI/CD (Continuous Integration/Continuous Delivery) pipeline is a powerful tool for efficiently deploying and managing applications in Kubernetes clusters.

It is a workflow process that automates the building, testing, and deployment of containerized applications in Kubernetes environments.

Efficiently Deploy and Manage Applications with Kubernetes CI/CD Pipeline - A Comprehensive Overview.

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Kubernetes Node Tutorial for Beginners | Kubernetes Node Explained

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[agapi-kalyptei]

Hi!! I'm the anon who sent @/jv the question about how tumblr is handling boops, thanks for answering it in detail i really appreciate it!!! I understand some of it but there's room to learn and I'll look forward to that.

can I ask a follow up question, i don't know if this makes sense but is it possible to use something like k8s containers instead of lots of servers for this purpose?

Hi! Thanks for reaching out.

Yeah my bad, I didn't know what your technical skill level is, so I wasn't writing it in a very approachable level.

The main takeaway is, high scalability has to happen on all levels - feature design, software architecture, networking, hardware, software, and software management.

K8s (an open source software project called Kubernetes, for the normal people) is on the "software management" category. It's like what MS Outlook or Google Calendar is to meetings. It doesn't do the meetings for you, it doesn't give you more time or more meeting rooms, but it gives you a way to say who goes where, and see which rooms are booked.

While I cannot say for Tumblr, I think I've heard they use Kubernetes at least in some parts of the stack, I can't speak for them. I can speak for myself tho! Been using K8s in production since 2015.

Once you want to run more than "1 redis 1 database 1 app" kind of situation, you will likely benefit from using K8s. Whether you have just a small raspberry pi somewhere, a rented consumer-grade server from Hetzner, or a few thousand machines, K8s can likely help you manage software.

So in short: yes, K8s can help with scalability, as long as the overall architecture doesn't fundamentally oppose getting scaled. Meaning, if you would have a central database for a hundred million of your users, and it becomes a bottleneck, then no amount of microservices serving boops, running with or without K8s, will not remove that bottleneck.

"Containers", often called Docker containers (although by default K8s has long stopped using Docker as a runtime, and Docker is mostly just something devs use to build containers) are basically a zip file with some info about what to run on start. K8s cannot be used without containers.

You can run containers without K8s, which might make sense if you're very hardware resource restricted (i.e. a single Raspberry Pi, developer laptop, or single-purpose home server). If you don't need to manage or monitor the cluster (i.e. the set of apps/servers that you run), then you don't benefit a lot from K8s.

Kubernetes is handy because you can basically do this (IRL you'd use some CI/CD pipeline and not do this from console, but conceptually this happens) -

kubectl create -f /stuff/boop_service.yaml kubectl create -f /stuff/boop_ingress.yaml kubectl create -f /stuff/boop_configmap.yaml kubectl create -f /stuff/boop_deploy.yaml

(service is a http endpoint, ingress is how the service will be available from outside of the cluster, configmap is just a bunch of settings and config files, and deploy is the thing that manages the actual stuff running)

At this hypothetical point, Tumblr stuff deploys, updates and tests the boop service before 1st April, generally having some one-click deploy feature in Jenkins or Spinnaker or similar. After it's tested and it's time to bring in the feature to everyone, they'd run

kubectl scale deploy boop --replicas=999

and wait until it downloads and runs the boop server on however many servers. Then they either deploy frontend to use this, or more likely, the frontend code is already live, and just displays boop features based on server time, or some server settings endpoint which just says "ok you can show boop now".

And then when it's over and they disable it in frontend, just again kubectl scale .. --replicas=10 to mop up whichever people haven't refreshed frontend and still are trying to spam boops.

This example, of course, assumes that "boop" is a completely separate software package/server, which is about 85/15% chance that it isn't, and more likely it's just one endpoint that they added to their existing server code, and is already running on hundreds of servers. IDK how Tumblr manages the server side code at all, so it's all just guesses.

Hope this was somewhat interesting and maybe even helpful! Feel free to send more asks.

[i-love-linux-and-reject-gender]

I've had a semi irrational fear of continer software (docker, cubernates etc) for a while, and none of my self hosting needs have needed more than a one off docker setup occasionally but i always ditch it fairly quickly. Any reason to use kubernates you wanna soap box about? (Features, use cases, stuff u've used it for, anything)

the main reasons why i like Kubernetes are the same reasons why i like NixOS (my Kubernetes addiction started before my NixOS journey)

both are declarative, reproducible and solve dependency hell

i will separate this a bit,

advantages of container technologies (both plain docker but also kubernetes):

every container is self-contained which solves dependency problems and "works on my machine" problems. you can move a docker container from one computer to another and as long as the container version and the mounted files stay the same and it will behave in the same way

advantages of docker-compose and kubernetes:

declarativeness. the standard way of spinning up a container with `docker run image:tag` is in my opinion an anti pattern and should be avoided. it makes updating the container difficult and more painful than it needs to be. instead docker compose allows you to write a yaml file instead which configures your container. like this:

```

version: "3"

services:

myService:

image: "image:tag"

```

you can then start up the container with this config with `docker compose up`. with this you can save the setup for all your docker containers in config files. this already makes your setup quite portable which is very cool. it increases your reliability by quite a bit since you only need to run `docker compose up -d` to configure everything for an application. when you also have the config files for that application stored somewhere it's even better.

kubernetes goes even further. this is what a simple container deployment looks like: (i cut out some stuff, this isn't enough to even expose this app)

this sure is a lot of boilerplate, and it can get much worse. but this is very powerful when you want to make everything on your server declarative.

for example, my grafana storage is not persistent, which means whenever i restart my grafana container, all config data gets lost. however, i am storing my dashboards in git and have SSO set up, so kubernetes automatically adds the dashboards from git

the main point why i love kubernetes so much is the combination of a CI/CD pipeline with a declarative setup.

there is a software called ArgoCD which can read your kubernetes config files from git, check if the ones that you are currently using are identical to the ones in git and automatically applies the state from git to your kubernetes.

i completely forgot to explain of the main features of kubernetes:

kubernetes is a clustered software, you can use one or two or three or 100 computers together with it and use your entire fleet of computers as one unit with kubernetes. i have currently 3 machines and i don't even decide which machine runs which container, kubernetes decides that for me and automatically maintains a good resource spread. this can also protect from computer failures, if one computer fails, the containers just get moved to another host and you barely use any uptime. this works even better with clustered storage, where copies of your data are distributed around your cluster. this is also useful for updates, as you can easily reboot a server for updates without causing any downtime.

also another interesting design pattern is the architecture of how containers are managed. to create a new container, you usually create a deployment, which is a higher-level resource than a container and which creates containers for you. and the deployment will always make sure that there are enough containers running so the deployment specifications are met. therefore, to restart a container in kubernetes, you often delete it and let the deployment create a new one.

so for use cases, it is mostly useful if you have multiple machines. however i have run kubernetes on a singular machine multiple times, the api and config is just faaaaaaar too convenient for me. you can run anything that can run in docker on kubernetes, which is (almost) everything. kubernetes is kind of a data center operating system, it makes stuff which would require a lot of manual steps obsolete and saves ops people a lot of time. i am managing ~150 containers with one interface with ease. and that amount will grow even more in the future lol

i hope this is what you wanted, this came straight from my kubernetes-obsessed brain. hope this isn't too rambly or annoying

it miiiiiiiight be possible to tell that this is my main interest lol

[greenoperator]

Microsoft Azure Fundamentals AI-900 (Part 5)

Microsoft Azure AI Fundamentals: Explore visual studio tools for machine learning

What is machine learning? A technique that uses math and statistics to create models that predict unknown values

Types of Machine learning

Regression - predict a continuous value, like a price, a sales total, a measure, etc

Classification - determine a class label.

Clustering - determine labels by grouping similar information into label groups

x = features

y = label

Azure Machine Learning Studio

You can use the workspace to develop solutions with the Azure ML service on the web portal or with developer tools

Web portal for ML solutions in Sure

Capabilities for preparing data, training models, publishing and monitoring a service.

First step assign a workspace to a studio.

Compute targets are cloud-based resources which can run model training and data exploration processes

Compute Instances - Development workstations that data scientists can use to work with data and models

Compute Clusters - Scalable clusters of VMs for on demand processing of experiment code

Inference Clusters - Deployment targets for predictive services that use your trained models

Attached Compute - Links to existing Azure compute resources like VMs or Azure data brick clusters

What is Azure Automated Machine Learning

Jobs have multiple settings

Provide information needed to specify your training scripts, compute target and Azure ML environment and run a training job

Understand the AutoML Process

ML model must be trained with existing data

Data scientists spend lots of time pre-processing and selecting data

This is time consuming and often makes inefficient use of expensive compute hardware

In Azure ML data for model training and other operations are encapsulated in a data set.

You create your own dataset.

Classification (predicting categories or classes)

Regression (predicting numeric values)

Time series forecasting (predicting numeric values at a future point in time)

After part of the data is used to train a model, then the rest of the data is used to iteratively test or cross validate the model

The metric is calculated by comparing the actual known label or value with the predicted one

Difference between the actual known and predicted is known as residuals; they indicate amount of error in the model.

Root Mean Squared Error (RMSE) is a performance metric. The smaller the value, the more accurate the model’s prediction is

Normalized root mean squared error (NRMSE) standardizes the metric to be used between models which have different scales.

Shows the frequency of residual value ranges.

Residuals represents variance between predicted and true values that can’t be explained by the model, errors

Most frequently occurring residual values (errors) should be clustered around zero.

You want small errors with fewer errors at the extreme ends of the sale

Should show a diagonal trend where the predicted value correlates closely with the true value

Dotted line shows a perfect model’s performance

The closer to the line of your model’s average predicted value to the dotted, the better.

Services can be deployed as an Azure Container Instance (ACI) or to a Azure Kubernetes Service (AKS) cluster

For production AKS is recommended.

Identify regression machine learning scenarios

Regression is a form of ML

Understands the relationships between variables to predict a desired outcome

Predicts a numeric label or outcome base on variables (features)

Regression is an example of supervised ML

What is Azure Machine Learning designer

Allow you to organize, manage, and reuse complex ML workflows across projects and users

Pipelines start with the dataset you want to use to train the model

Each time you run a pipelines, the context(history) is stored as a pipeline job

Encapsulates one step in a machine learning pipeline.

Like a function in programming

In a pipeline project, you access data assets and components from the Asset Library tab

You can create data assets on the data tab from local files, web files, open at a sets, and a datastore

Data assets appear in the Asset Library

Azure ML job executes a task against a specified compute  target.

Jobs allow systematic tracking of your ML experiments and workflows.

Understand steps for regression

To train a regression model, your data set needs to include historic features and known label values.

Use the designer’s Score Model component to generate the predicted class label value

Connect all the components that will run in the experiment

Average difference between predicted and true values

It is based on the same unit as the label

The lower the value is the better the model is predicting

The square root of the mean squared difference between predicted and true values

Metric based on the same unit as the label.

A larger difference indicates greater variance in the individual  label errors

Relative metric between 0 and 1 on the square based on the square of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Since the value is relative, it can compare different models with different label units

Relative metric between 0 and 1 on the square based on the absolute of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Can be used to compare models where the labels are in different units

Also known as R-squared

Summarizes how much variance exists between predicted and true values

Closer to 1 means the model is performing better

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a classification model with Azure ML designer

Classification is a form of ML used to predict which category an item belongs to

Like regression this is a supervised ML technique.

Understand steps for classification

True Positive - Model predicts the label and the label is correct

False Positive - Model predicts wrong label and the data has the label

False Negative - Model predicts the wrong label, and the data does have the label

True Negative - Model predicts the label correctly and the data has the label

For multi-class classification, same approach is used. A model with 3 possible results would have a 3x3 matrix.

Diagonal lien of cells were the predicted and actual labels match

Number of cases classified as positive that are actually positive

True positives divided by (true positives + false positives)

Fraction of positive cases correctly identified

Number of true positives divided by (true positives + false negatives)

Overall metric that essentially combines precision and recall

Classification models predict probability for each possible class

For binary classification models, the probability is between 0 and 1

Setting the threshold can define when a value is interpreted as 0 or 1.  If its set to 0.5 then 0.5-1.0 is 1 and 0.0-0.4 is 0

Recall also known as True Positive Rate

Has a corresponding False Positive Rate

Plotting these two metrics on a graph for all values between 0 and 1 provides information.

Receiver Operating Characteristic (ROC) is the curve.

In a perfect model, this curve would be high to the top left

Area under the curve (AUC).

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a Clustering model with Azure ML designer

Clustering is used to group similar objects together based on features.

Clustering is an example of unsupervised learning, you train a model to just separate items based on their features.

Understanding steps for clustering

Prebuilt components exist that allow you to clean the data, normalize it, join tables and more

Requires a dataset that includes multiple observations of the items you want to cluster

Requires numeric features that can be used to determine similarities between individual cases

Initializing K coordinates as randomly selected points called centroids in an n-dimensional space (n is the number of dimensions in the feature vectors)

Plotting feature vectors as points in the same space and assigns a value how close they are to the closes centroid

Moving the centroids to the middle points allocated to it (mean distance)

Reassigning to the closes centroids after the move

Repeating the last two steps until tone.

Maximum distances between each point and the centroid of that point’s cluster.

If the value is high it can mean that cluster is widely dispersed.

With the Average Distance to Closer Center, we can determine how spread out the cluster is

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

[korshubudemycoursesblog]

Docker Kubernetes: Simplifying Container Management and Scaling with Ease

If you're diving into the world of containerization, you've probably come across terms like Docker and Kubernetes more times than you can count. These two technologies are the backbone of modern software development, especially when it comes to creating scalable, efficient, and manageable applications. Docker Kubernetes are often mentioned together because they complement each other so well. But what exactly do they do, and why are they so essential for developers today?

In this blog, we’ll walk through the essentials of Docker Kubernetes, exploring why they’re a game-changer in managing and scaling applications. By the end, you’ll have a clear understanding of how they work together and how learning about them can elevate your software development journey.

What Is Docker?

Let’s start with Docker. It’s a tool designed to make it easier to create, deploy, and run applications by using containers. Containers package up an application and its dependencies into a single, lightweight unit. Think of it as a portable environment that contains everything your app needs to run, from libraries to settings, without relying on the host’s operating system.

Using Docker means you can run your application consistently across different environments, whether it’s on your local machine, on a virtual server, or in the cloud. This consistency reduces the classic “it works on my machine” issue that developers often face.

Key Benefits of Docker

Portability: Docker containers can run on any environment, making your applications truly cross-platform.

Efficiency: Containers are lightweight and use fewer resources compared to virtual machines.

Isolation: Each container runs in its isolated environment, meaning fewer compatibility issues.

Understanding Kubernetes

Now that we’ve covered Docker, let’s move on to Kubernetes. Developed by Google, Kubernetes is an open-source platform designed to manage containerized applications across a cluster of machines. In simple terms, it takes care of scaling and deploying your Docker containers, making sure they’re always up and running as needed.

Kubernetes simplifies the process of managing multiple containers, balancing loads, and ensuring that your application stays online even if parts of it fail. If Docker helps you create and run containers, Kubernetes helps you manage and scale them across multiple servers seamlessly.

Key Benefits of Kubernetes

Scalability: Easily scale applications up or down based on demand.

Self-Healing: If a container fails, Kubernetes automatically replaces it with a new one.

Load Balancing: Kubernetes distributes traffic evenly to avoid overloading any container.

Why Pair Docker with Kubernetes?

When combined, Docker Kubernetes provide a comprehensive solution for modern application development. Docker handles the packaging and containerization of your application, while Kubernetes manages these containers at scale. For businesses and developers, using these two tools together is often the best way to streamline development, simplify deployment, and manage application workloads effectively.

For example, if you’re building a microservices-based application, you can use Docker to create containers for each service and use Kubernetes to manage those containers. This setup allows for high availability and easier maintenance, as each service can be updated independently without disrupting the rest of the application.

Getting Started with Docker Kubernetes

To get started with Docker Kubernetes, you’ll need to understand the basic architecture of each tool. Here’s a breakdown of some essential components:

1. Docker Images and Containers

Docker Image: The blueprint for your container, containing everything needed to run an application.

Docker Container: The running instance of a Docker Image, isolated and lightweight.

2. Kubernetes Pods and Nodes

Pod: The smallest unit in Kubernetes that can host one or more containers.

Node: A physical or virtual machine that runs Kubernetes Pods.

3. Cluster: A group of nodes working together to run containers managed by Kubernetes.

With this setup, Docker Kubernetes enable seamless deployment, scaling, and management of applications.

Key Use Cases for Docker Kubernetes

Microservices Architecture

By separating each function of an application into individual containers, Docker Kubernetes make it easy to manage, deploy, and scale each service independently.

Continuous Integration and Continuous Deployment (CI/CD)

Docker Kubernetes are often used in CI/CD pipelines, enabling fast, consistent builds, testing, and deployment.

High Availability Applications

Kubernetes ensures your application remains available, balancing traffic and restarting containers as needed.

DevOps and Automation

Docker Kubernetes play a central role in the DevOps process, supporting automation, efficiency, and flexibility.

Key Concepts to Learn in Docker Kubernetes

Container Orchestration: Learning how to manage containers efficiently across a cluster.

Service Discovery and Load Balancing: Ensuring users are directed to the right container.

Scaling and Self-Healing: Automatically adjusting the number of containers and replacing failed ones.

Best Practices for Using Docker Kubernetes

Resource Management: Define resources for each container to prevent overuse.

Security: Use Kubernetes tools like Role-Based Access Control (RBAC) and secrets management.

Monitor and Optimize: Use monitoring tools like Prometheus and Grafana to keep track of performance.

Conclusion: Why Learn Docker Kubernetes?

Whether you’re a developer or a business, adopting Docker Kubernetes can significantly enhance your application’s reliability, scalability, and performance. Learning Docker Kubernetes opens up possibilities for building robust, cloud-native applications that can scale with ease. If you’re aiming to create applications that need to handle high traffic and large-scale deployments, there’s no better combination.

Docker Kubernetes offers a modern, efficient way to develop, deploy, and manage applications in today's fast-paced tech world. By mastering these technologies, you’re setting yourself up for success in a cloud-driven, containerized future.

[cloudastra1]

Kubernetes, the popular open-source container orchestration platform, offers robust features for automating the deployment, scaling, and management of containerized applications. However, its powerful capabilities come with a complex security landscape that requires careful consideration to protect applications and data. Here’s an overview of key practices and tools to enhance Kubernetes security:

1. Network Policies

Network policies in Kubernetes control the communication between pods. By default, Kubernetes allows all traffic between pods, but network policies can be used to define rules that restrict which pods can communicate with each other. This is crucial for minimizing the attack surface and preventing unauthorized access.

2. RBAC (Role-Based Access Control)

Kubernetes RBAC is a method for regulating access to the Kubernetes API. It allows you to define roles with specific permissions and assign those roles to users or service accounts. Implementing RBAC helps ensure that users and applications have only the permissions they need to function, reducing the risk of privilege escalation.

3. Secrets Management

Kubernetes Secrets are designed to store sensitive information, such as passwords, OAuth tokens, and SSH keys. It’s essential to use Secrets instead of environment variables for storing such data to ensure it’s kept secure. Additionally, consider integrating with external secret management tools like HashiCorp Vault for enhanced security.

4. Pod Security Policies

Pod Security Policies (PSPs) are cluster-level resources that control security-sensitive aspects of pod specifications. PSPs can enforce restrictions on pod execution, such as requiring the use of specific security contexts, preventing the use of privileged containers, and controlling access to host resources. While PSPs are being deprecated in favor of other mechanisms like OPA Gatekeeper, they are still crucial for current security practices.

5. Image Security

Ensuring the security of container images is critical. Use trusted base images, and regularly scan your images for vulnerabilities using tools like Clair or Trivy. Additionally, sign your images with tools like Notary and use a container registry that supports image signing and verification.

6. Runtime Security

Monitoring your containers at runtime is essential to detect and respond to security threats. Tools like Falco, a runtime security tool for Kubernetes, can help detect unexpected behavior, configuration changes, and potential intrusions. Integrating such tools with a logging and alerting system ensures that any suspicious activity is promptly addressed.

7. Secure Configuration

Ensure your Kubernetes components are securely configured. For example, restrict API server access, use TLS for secure communication between components, and regularly review and audit your configurations. Tools like kube-bench can help automate the process of checking your cluster against security best practices.

8. Regular Updates and Patching

Keeping your Kubernetes environment up-to-date is critical for maintaining security. Regularly apply patches and updates to Kubernetes components, container runtimes, and the underlying operating system to protect against known vulnerabilities.

9. Audit Logs

Enable Kubernetes audit logs to track access and modifications to the cluster. Audit logs provide a detailed record of user actions, making it easier to detect and investigate suspicious activities. Integrate these logs with a centralized logging system for better analysis and retention.

10. Compliance and Best Practices

Adhering to security best practices and compliance requirements is essential for any Kubernetes deployment. Regularly review and align your security posture with standards such as NIST, CIS Benchmarks, and organizational policies to ensure your cluster meets necessary security requirements.

In conclusion, Kubernetes security is multi-faceted and requires a comprehensive approach that includes network policies, access controls, secrets management, and regular monitoring. By implementing these best practices and leveraging the right tools, you can significantly enhance the security of your Kubernetes environment, ensuring your applications and data remain protected against threats.

[internsipgate]

Top 7 Essential DevOps Tools Every Intern Should Know for Success

In the fast-paced world of software development, DevOps is a crucial bridge between development and operations. As an intern diving into this field, learning the right tools can give you a competitive edge, boost your productivity, and help you stand out. Whether you're collaborating with teams, automating tasks, or ensuring smooth deployments, understanding DevOps tools is essential.

Here, we’ll break down the top 7 DevOps tools every intern should know. These tools cover everything from continuous integration and deployment to infrastructure management and monitoring. Let's get started!https://internshipgate.com

1. Git: Version Control Done Right

Git is the backbone of version control systems. It allows multiple developers to work on the same project, track changes, and manage code versions efficiently.

Why You Need Git: As an intern, you’ll need to collaborate with others, and Git helps maintain order in a project's history by allowing you to revert to earlier versions when necessary.

Key Features: Branching, merging, commit history, and pull requests.

How to Get Started: Tools like GitHub or GitLab provide a user-friendly interface for Git, offering repositories to store your code, collaborate, and manage projects.

2. Jenkins: Automate Your Workflow

Jenkins is one of the most popular automation tools for continuous integration and continuous delivery (CI/CD). It automates repetitive tasks, such as building, testing, and deploying applications.

Why You Need Jenkins: Automation is at the heart of DevOps. Jenkins allows you to automate the testing and deployment process, reducing human errors and ensuring faster releases.

Key Features: Plugin support, pipeline as code, and easy configuration.

How to Get Started: As an intern, start by setting up simple pipelines and exploring Jenkins plugins to automate various development processes.

3. Docker: Containerization Made Simple

Docker enables developers to package applications into containers—standardized units that contain everything the application needs to run. This ensures consistency across environments, whether it's on your laptop or in production.

Why You Need Docker: Containerization simplifies the deployment process by eliminating the classic "it works on my machine" problem. You'll ensure consistency from development through to production.

Key Features: Lightweight containers, easy scaling, and isolation.

How to Get Started: Experiment by creating a Dockerfile for your project, building containers, and deploying them to services like Docker Hub or Kubernetes.

4. Kubernetes: Orchestrating Containers

Once you understand Docker, the next step is Kubernetes, a powerful orchestration tool that manages containerized applications across multiple hosts.

Why You Need Kubernetes: For large-scale projects, simply running containers isn’t enough. Kubernetes automates the deployment, scaling, and management of containerized applications, ensuring high availability.

Key Features: Load balancing, self-healing, and auto-scaling.

How to Get Started: Start by deploying a small application on a local Kubernetes cluster using Minikube and scaling it as you go.

5. Ansible: Automate Infrastructure Management

Ansible is a popular tool for automating infrastructure tasks. It simplifies complex tasks like application deployment, configuration management, and orchestration.

Why You Need Ansible: Ansible uses a simple, human-readable language (YAML) to automate repetitive tasks. For interns, it’s a great tool to learn how infrastructure is managed.

Key Features: Agentless, idempotent, and easy to learn.

How to Get Started: Set up basic Ansible playbooks to automate tasks like server setup or application deployment.

6. Terraform: Infrastructure as Code (IaC)

Terraform is a tool for creating, managing, and deploying infrastructure resources using a declarative configuration language.

Why You Need Terraform: With Terraform, you can automate infrastructure provisioning, ensuring that environments are consistent, scalable, and repeatable. It’s a key tool in DevOps for managing cloud resources.

Key Features: Cross-platform support, infrastructure state management, and modularity.

How to Get Started: Start by writing simple Terraform scripts to provision cloud resources like virtual machines or storage on platforms like AWS or Google Cloud.

7. Prometheus and Grafana: Monitoring and Visualization

DevOps is not just about deployment; it’s also about maintaining and monitoring the health of your applications and infrastructure. Prometheus and Grafana are the go-to tools for monitoring and visualization.

Why You Need Prometheus and Grafana: Monitoring ensures that you catch issues before they affect users. Prometheus collects metrics from your systems, while Grafana visualizes them, providing insights into system performance.

Key Features: Time-series data collection (Prometheus) and customizable dashboards (Grafana).

How to Get Started: Start with setting up Prometheus to collect basic metrics and use Grafana to create dashboards for visualizing CPU usage, memory, and request rates.

FAQs

What is the difference between Docker and Kubernetes? Docker is used for creating containers, which are lightweight and portable environments for running applications. Kubernetes, on the other hand, manages and orchestrates those containers across multiple machines, handling tasks like load balancing, scaling, and self-healing.

Why is version control important in DevOps? Version control, like Git, allows multiple developers to work on the same codebase without conflicting changes. It tracks changes, facilitates collaboration, and helps revert to previous versions if necessary, ensuring a smooth workflow.

How does Jenkins improve software development? Jenkins automates repetitive tasks such as testing and deployment, reducing manual effort, minimizing errors, and speeding up the release process through continuous integration and delivery (CI/CD).

Is Ansible better than Terraform for infrastructure management? Ansible and Terraform serve different purposes. Ansible is better for configuration management and automation, while Terraform excels in infrastructure provisioning and management. Many DevOps teams use both together.

Can I use Prometheus without Grafana? Yes, Prometheus can be used without Grafana, but it is often paired with Grafana for better visualization. Prometheus collects the metrics, and Grafana helps you analyze them with interactive dashboards.

How can interns start learning these DevOps tools? Start small by experimenting with free tutorials, hands-on labs, and online courses. Use cloud-based platforms like GitHub, AWS, or Google Cloud to practice with these tools in real-world scenarios.

Conclusion

Mastering these essential DevOps tools will set you up for success in your DevOps journey. As an intern, focusing on learning these tools will not only enhance your technical skills but also improve your ability to collaborate with teams and manage complex systems. Whether it's automating workflows with Jenkins or orchestrating containers with Kubernetes, each tool plays a critical role in modern software development and operations.https://internshipgate.com

[web-age-solutions]

Transforming Infrastructure with Automation: The Power of Terraform and AWS Elastic Kubernetes Service Training

In the digital age, organizations are modernizing their infrastructure and shifting to cloud-native solutions. Terraform automates infrastructure provisioning across multiple cloud providers, while AWS Elastic Kubernetes Service (EKS) orchestrates containers, enabling businesses to manage scalable, high-availability applications. Together, these technologies form a foundation for managing dynamic systems at scale. To fully leverage them, professionals need practical, hands-on skills. This is where Elastic Kubernetes Services training becomes essential, offering expertise to automate and manage containerized applications efficiently, ensuring smooth operations across complex cloud infrastructures. 

Why Automation Matters in Cloud Infrastructure 

As businesses scale, manual infrastructure management becomes inefficient and prone to errors, especially in large, multi-cloud environments. Terraform, as an infrastructure-as-code (IaC) tool, automates provisioning, networking, and deployments, eliminating repetitive manual tasks and saving time. When paired with AWS Elastic Kubernetes Service (EKS), automation improves reliability and scalability, optimizes resource use, minimizes downtime, and significantly enhances deployment velocity for businesses operating in a cloud-native ecosystem. 

The Role of Terraform in Automating AWS 

Terraform simplifies cloud infrastructure by codifying resources into reusable, version-controlled configuration files, ensuring consistency and reducing manual effort across environments. In AWS, Terraform automates critical services such as EC2 instances, VPCs, and RDS databases. Integrated with Elastic Kubernetes Services (EKS), Terraform automates the lifecycle of Kubernetes clusters—from creating clusters to scaling applications across availability zones—allowing seamless cloud deployment and enhancing automation efficiency across diverse environments.   

How AWS Elastic Kubernetes Service Elevates Cloud Operations 

AWS Elastic Kubernetes Service (EKS) simplifies deploying, managing, and scaling Kubernetes applications by offering a fully managed control plane that takes the complexity out of Kubernetes management. When combined with Terraform, this automation extends even further, allowing infrastructure to be defined, deployed, and updated with minimal manual intervention. Elastic Kubernetes services training equips professionals to master this level of automation, from scaling clusters dynamically to managing workloads and applying security best practices in a cloud environment. 

Benefits of Elastic Kubernetes Services Training for Professionals 

Investing in Elastic Kubernetes Services training goes beyond managing Kubernetes clusters; it’s about gaining the expertise to automate and streamline cloud infrastructure efficiently. This training enables professionals to: 

Increase Operational Efficiency: Automating repetitive tasks allows teams to focus on innovation rather than managing infrastructure manually, improving productivity across the board.  

Scale Applications Seamlessly: Understanding how to leverage EKS ensures that applications can scale with demand, handling traffic spikes without sacrificing performance or reliability.  

Stay Competitive: With cloud technologies evolving rapidly, staying up-to-date on tools like Terraform and EKS gives professionals a significant edge, allowing them to meet modern business demands effectively. 

Driving Innovation with Automation 

Automation is essential for businesses seeking to scale and remain agile in a competitive digital landscape. Terraform and AWS Elastic Kubernetes Service (EKS) enable organizations to automate infrastructure management and deploy scalable, resilient applications. Investing in Elastic Kubernetes Services training with Web Age Solutions equips professionals with technical proficiency and practical skills, positioning them as key innovators in their organizations while building scalable cloud environments that support long-term growth and future technological advancements. 

For more information visit: https://www.webagesolutions.com/courses/WA3108-automation-with-terraform-and-aws-elastic-kubernetes-service

[virtualizationhowto]

Lens Kubernetes: Simple Cluster Management Dashboard and Monitoring

Lens Kubernetes: Simple Cluster Management Dashboard and Monitoring #homelab #kubernetes #KubernetesManagement #LensKubernetesDesktop #KubernetesClusterManagement #MultiClusterManagement #KubernetesSecurityFeatures #KubernetesUI #kubernetesmonitoring

Kubernetes is a well-known container orchestration platform. It allows admins and organizations to operate their containers and support modern applications in the enterprise. Kubernetes management is not for the “faint of heart.” It requires the right skill set and tools. Lens Kubernetes desktop is an app that enables managing Kubernetes clusters on Windows and Linux devices. Table of…

[ericvanderburg]

Akamai App Platform reduces the complexity associated with managing Kubernetes clusters

http://securitytc.com/TG7tH5

[codeonedigest]

Kubernetes Cloud Controller Manager Tutorial for Beginners

Hi, a new #video on #kubernetes #cloud #controller #manager is published on #codeonedigest #youtube channel. Learn kubernetes #controllermanager #apiserver #kubectl #docker #proxyserver #programming #coding with #codeonedigest #kubernetescontrollermanag

Kubernetes is a popular open-source platform for container orchestration. Kubernetes follows client-server architecture and Kubernetes cluster consists of one master node with set of worker nodes.  Cloud Controller Manager is part of Master node. Let’s understand the key components of master node. etcd is a configuration database stores configuration data for the worker nodes. API Server to…

[govindhtech]

How DNS-Based Endpoints Enhance Security in GKE Clusters

DNS-Based Endpoints

In order to prevent unwanted access while maintaining cluster management, it is crucial to restrict access to the cluster control plane, which processes Kubernetes API calls, as you are aware if you use Google Kubernetes Engine (GKE).

Authorized networks and turning off public endpoints were the two main ways that GKE used to secure the control plane. However, accessing the cluster may be challenging when employing these techniques. To obtain access through the cluster’s private network, you need to come up with innovative solutions like bastion hosts, and the list of permitted networks needs to be updated for every cluster.

Google Cloud is presenting a new DNS-based endpoint for GKE clusters today, which offers more security restrictions and access method flexibility. All clusters have the DNS-based endpoint available today, irrespective of cluster configuration or version. Several of the present issues with Kubernetes control plane access are resolved with the new DNS-based endpoint, including:

Complex allowlist and firewall setups based on IP: ACLs and approved network configurations based on IP addresses are vulnerable to human setup error.

IP-based static configurations: You must adjust the approved network IP firewall configuration in accordance with changes in network configuration and IP ranges.

Proxy/bastion hosts: You must set up a proxy or bastion host if you are accessing the GKE control plane from a different cloud location, a distant network, or a VPC that is not the same as the VPC where the cluster is located.

Due to these difficulties, GKE clients now have to deal with a complicated configuration and a perplexing user experience.

Introducing a new DNS-based endpoint

Any network that can connect to Google Cloud APIs, such as VPC networks, on-premises networks, or other cloud networks, can access the frontend that the DNS name resolves to. This front-end Each cluster control plane has its own DNS or fully qualified domain name (FQDN) with the new DNS-based endpoint for GKE routes traffic to your cluster after using security policies to block unwanted traffic.Image credit to Google cloud

This strategy has several advantages:

Simple flexible access from anywhere

Proxy nodes and bastion hosts are not required when using the DNS-based endpoint. Without using proxies, authorized users can access your control plane from various clouds, on-premises deployments, or from their homes. Transiting various VPCs is unrestricted with DNS-based endpoints because all that is needed is access to Google APIs. You can still use VPC Service Controls to restrict access to particular networks if you’d like.

Dynamic Security

The same IAM controls that safeguard all GCP API access are also utilized to protect access to your control plane over the DNS-based endpoint. You can make sure that only authorized users, regardless of the IP address or network they use, may access the control plane by implementing identity and access management (IAM) policies. You can easily remove access to a specific identity if necessary, without having to bother about network IP address bounds and configuration. IAM roles can be tailored to the requirements of your company.

See Customize your network isolation for additional information on the precise permissions needed to set up IAM roles, rules, and authentication tokens.

Two layers of security

You may set up network-based controls with VPC Service Controls in addition to IAM policies, giving your cluster control plane a multi-layer security architecture. Context-aware access controls based on network origin and other attributes are added by VPC Service Controls. The security of a private cluster that is only accessible from a VPC network can be equaled.

All Google Cloud APIs use VPC Service Controls, which ensures that your clusters’ security setup matches that of the services and data hosted by all other Google Cloud APIs. For all Google Cloud resources used in a project, you may provide solid assurances for the prevention of illegal access to data and services. Cloud Audit Logs and VPC Service Controls work together to track control plane access.

How to configure DNS-based access

The procedure of setting up DNS-based access for the GKE cluster control plane is simple Check the next steps.

Enable the DNS-based endpoint

Use the following command to enable DNS-based access for a new cluster:

$ gcloud container clusters create $cluster_name –enable-dns-access

As an alternative, use the following command to allow DNS-based access for an existing cluster:

$ gcloud container clusters update $cluster_name –enable-dns-acces

Configure IAM

Requests must be authenticated with a role that has the new IAM authorization in order to access the control plane.

roles/container.developer

roles/container.viewer

Ensure your client can access Google APIs

You must confirm that your client has access to Google APIs if it is connecting from a Google VPC. Activating Private Google Access, which enables clients to connect to Google APIs without using the public internet, is one approach to accomplish this. Each subnet has its own configuration for private Google Access.

Tip: Private Google Access is already enabled for node subnetworks.

[Selective] Setting up access to Google APIs via Private Service Connect

The Private Service Connect for Google APIs endpoint, which is used to access the other Google APIs, can be used to access the DNS endpoint of the cluster. To configure Private Service Connect for Google APIs endpoints, follow the instructions on the Access Google APIs through endpoints page.

Since using a custom endpoint to access the cluster’s DNS is not supported, as detailed in the use an endpoint section, in order to get it to work, you must create a CNAME to “gke.goog” and an A record between “gke.goog” and the private IP allocated to Private Service Connect for Google APIs.

Try DNS access

You can now try DNS-based access. The following command generates a kubeconfig file using the cluster’s DNS address:

gcloud container clusters get-credentials $cluster_name –dns-endpoint

Use kubectl to access your cluster. This allows Cloud Shell to access clusters without a public IP endpoint, previously required a proxy.

Extra security using VPC Service Controls

Additional control plane access security can be added with VPC Service Controls.

What about the IP-based endpoint?

You can test DNS-based control plane access without affecting your clients by using the IP-based endpoint. After you’re satisfied with DNS-based access, disable IP-based access for added security and easier cluster management:

gcloud container clusters update $cluster_name –enable-ip-access=false

Read more on Govindhtech.com

[fromdevcom]

Are you exploring Kubernetes or considering using it? If yes, then you will be thankful you landed on this article about a little pondered but crucial component of Kubernetes: labels and annotations. As we know, Kubernetes is an orchestration tool that is usually used to manage containerized applications. In this article, we will be understanding labels and annotations are so important for managing containerized applications.Introduction to Labels and AnnotationsLabels are used in configuration files to specify attributes of objects that are meaningful and relevant to the user, especially in the grouping, viewing, and performing operations. In addition, labels can be used in the specs and metadata sections.Annotations, on the other hand, help provide a place to store non-identifying metadata, which may be used to elaborate on the context of an object.The following are some Kubernetes labels:name: Name of the applicationinstance: unique name of the instanceversion: semantic version numbercomponent: the component within your logical architecturepart-of: the name of the higher-level application this object is a part of.managed-by: the person who manages it.Note: Labels and selectors work together to identify groups of relevant resources. This procedure must be efficient because selectors are used to querying labels. Labels are always restricted by RFC 1123 to ensure efficient queries. Labels are limited to a maximum of 63 characters by RFC 1123, among other restrictions. When you want Kubernetes to group a set of relevant resources, you should use labels.LabelsLet’s dive in a little further. Labels are value pairs - like pods - that are attached to objects. They’re used to specify identifying attributes of objects which are relevant to users. They do not affect the semantics of the core system in any way - labels are just used to organize subsets of objects. Here, I have created labels using kubectl:Now, the question that arises is this: Why should we use labels in Kubernetes? This is the question most people ask, and so is one of the two main questions of this article.The main benefits of using labels in Kubernetes include help in organizing Kubernetes workloads in the clusters, mapping our own organizational structures into system objects, selecting or querying a specific item, grouping objects and accessing them when required, and finally enabling us to select anything we want and execute it in kubectl.You can use labels and annotations to attach metadata to objects – labels, in particular, can be used to select objects and find objects or collections of objects that satisfy certain criteria. Annotations, however, are not used for identification. Let’s look at annotations a bit more.AnnotationsNow, let us turn to the other question this article wishes to address: Why do we use annotations in Kubernetes?To answer this, let us first look at this photo:As you can see, I have created an image registry and specified the link. Now, in case of anything changes, I can track the changes in the URL specified. This is how we use annotations. One of the most frequently used examples for explaining the need for annotations is comparing it to storing phone numbers. The main benefits of using annotations in Kubernetes include helping us in elaborating the context at hand, the ability to track changes, communicating to the scheduler in Kubernetes about the scheduling policy, and keeping track of the replicates we have created. Kubernetes scheduling is the process of assigning pods to matched nodes in a cluster. The scheduler watches for newly created pods and assigns them to the best possible node based on scheduling principles and configuration options. In addition to that, annotations help us in deployment so that we can track replica sets, they help DevOps teams provide context and keep useful context, and they provide uniqueness, unlike labels.Some examples of information that can be recorded by annotations are fields; build, release, or image

information; pointers to logging; client libraries and tools; user or system provenance information; tool metadata; and directives from end-uses to implementations.ConclusionIn this article, we first went through the basic concept of what labels and annotations are. Then, we used kubectl, the most powerful and easy-to-use command-line tool for Kubernetes. Kubectl helps us query data in our Kubernetes cluster.As you can see, the use of labels and annotations in Kubernetes plays a key role in deployment. They not only help us add more information about our configuration files but also help other teams, especially the DevOps team, understand more about your files and their use in managing these applications.Thanks for reading. Let's keep building stuff together and learn a whole lot more every day! Stay tuned for more on Kubernetes, and happy learning!

[korshubudemycoursesblog]

Certified Kubernetes Application Developer: Your Step-by-Step Guide to Certification Success

Kubernetes has become a powerhouse in the tech world, reshaping how applications are deployed and managed. As more companies shift to containerized applications and cloud-native architectures, the demand for Certified Kubernetes Application Developers (CKADs) continues to grow. This certification is a gateway for anyone looking to prove their skills in Kubernetes application development and make a mark in DevOps.

But what does it mean to be a Certified Kubernetes Application Developer? What benefits does this certification offer, and how can you prepare effectively? Here’s a comprehensive guide designed to give you all the insights and steps you need to become CKAD certified.

What is the Certified Kubernetes Application Developer Certification?

The Certified Kubernetes Application Developer (CKAD) certification is designed for those who are proficient in designing, building, and managing applications in a Kubernetes environment. This certification focuses on real-world skills and validates your ability to deploy, troubleshoot, and manage applications on Kubernetes clusters.

Kubernetes, managed by the Cloud Native Computing Foundation (CNCF), has become the backbone of scalable applications, and the CKAD exam is recognized globally. With this certification, you’ll showcase a high level of competency in handling the complexities of Kubernetes, proving invaluable to companies adopting containerized solutions.

Why Choose the Certified Kubernetes Application Developer Certification?

Career Advancement: With a CKAD certification, you’ll have proof of your Kubernetes skills, which is crucial in today’s DevOps-centric job market. Companies are on the lookout for Certified Kubernetes Application Developers who can manage application lifecycles within Kubernetes clusters.

High Demand: Kubernetes has quickly become a standard in application deployment. With the Certified Kubernetes Application Developer certification, you are positioning yourself in one of the most sought-after areas of tech.

Competitive Edge: Many IT professionals may know Kubernetes, but a CKAD-certified developer has verified skills. The certification proves your expertise in managing containerized applications in production environments, a key asset for employers.

Global Recognition: The Certified Kubernetes Application Developer credential is recognized worldwide, making it a valuable addition to your resume regardless of your location.

Increased Salary Potential: Professionals with CKAD certification often earn higher salaries due to the specialized skills required. This certification not only opens doors but also ensures competitive compensation.

What Skills Will You Learn with the Certified Kubernetes Application Developer Certification?

The CKAD exam measures proficiency in the following areas, which are essential for every Kubernetes developer:

Application Design and Deployment: Learn how to design applications that can seamlessly run on Kubernetes. This includes understanding Kubernetes objects, such as Pods, Deployments, and ReplicaSets.

Application Observability: Monitoring is a key skill. You’ll gain knowledge in handling logs and metrics to keep track of applications, a crucial part of maintaining reliable systems.

Configuration: The CKAD certification covers Kubernetes configurations for applications, including secrets, config maps, and persistent storage.

Services & Networking: Master the art of connecting applications within and outside Kubernetes. This part covers Services, DNS, and Ingress controllers.

Troubleshooting: Being able to troubleshoot is critical. CKADs are skilled in diagnosing issues with deployments, Pods, and clusters, helping maintain smooth operations.

Preparing for the Certified Kubernetes Application Developer Exam: Tips and Resources

Here’s how you can effectively prepare for the Certified Kubernetes Application Developer certification:

1. Understand the Exam Format

The CKAD exam consists of multiple performance-based tasks to complete within two hours. It’s essential to practice under time constraints, as the exam tests your speed and accuracy in applying Kubernetes concepts. The tasks range across different domains, each with a specific weight. This real-world exam format means you should focus on hands-on practice.

2. Use Official CNCF Resources

CNCF provides free resources, including the Kubernetes Documentation and CKAD-specific guides. These resources will help you understand the core concepts, especially if you’re new to Kubernetes.

3. Enroll in a Kubernetes Course

Several courses are designed specifically for CKAD preparation. A Certified Kubernetes Application Developer course can guide you step-by-step through the skills required for the exam. Many platforms, including Udemy and Coursera, offer comprehensive CKAD prep courses that cover all domains in detail.

4. Practice, Practice, Practice

Kubernetes is a hands-on tool, so theoretical knowledge alone won’t help you. Create a Kubernetes cluster, practice deploying applications, and experiment with different Kubernetes components like Pods, Services, and ConfigMaps. Websites like Katacoda and Play with Kubernetes offer sandboxes where you can practice for free.

5. Review Common Commands and YAML Files

Understanding YAML syntax and common Kubernetes commands is crucial for the CKAD exam. The more comfortable you are with commands like kubectl run, kubectl apply, and kubectl expose, the faster you’ll be during the exam. Also, review how to work with YAML files to define Pods, Deployments, and other Kubernetes objects.

6. Test Yourself with Sample Questions

Sample questions and mock exams are invaluable. They not only test your knowledge but also help you familiarize yourself with the CKAD’s practical format. You can find sample exams in resources like Linux Academy or KodeKloud’s CKAD labs.

Exam Day: Tips for Success

Set Up Your Environment: Make sure your testing environment is stable and distraction-free. The exam requires focus, so ensure your workspace is ready.

Be Time-Conscious: The CKAD exam is time-bound, so aim to spend no more than 5–10 minutes on each task. If you’re stuck, move on and return later.

Use Built-In Documentation: The Kubernetes documentation is available during the exam. Use it to your advantage, but don’t rely too heavily on it. Knowing your commands and YAML structures beforehand will save you time.

Stay Calm: The pressure of a timed, hands-on exam can be intense. Trust your preparation, and remember to approach each question methodically.

What’s Next After Getting Your CKAD?

After earning your Certified Kubernetes Application Developer certification, you’re well on your way in the Kubernetes world! But Kubernetes is constantly evolving. Here are some next steps to deepen your expertise:

Certified Kubernetes Administrator (CKA): This certification focuses on Kubernetes administration. It’s ideal if you want to expand your knowledge beyond application development.

Specialized Kubernetes Tools: Explore Kubernetes tools like Helm, Istio, and Prometheus to further your containerization and monitoring skills. Many Kubernetes projects rely on these tools for advanced functionality.

Real-World Projects: Put your CKAD skills to use in real-world projects. Many employers value hands-on experience, so consider working on cloud-native projects that utilize Kubernetes.

Contribute to Open Source: Contributing to Kubernetes or related projects can further solidify your skills and enhance your resume. The Kubernetes community welcomes contributions from developers worldwide.

Conclusion: Becoming a Certified Kubernetes Application Developer Is a Game-Changer

Earning the Certified Kubernetes Application Developer certification isn’t just a feather in your cap; it’s a leap forward in your career. By mastering Kubernetes, you open doors to roles that involve cloud-native development, DevOps, and beyond. The demand for Certified Kubernetes Application Developers is only growing as organizations recognize the value of containerized applications.