#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.

[agapi-kalyptei]

How to be a senior developer, pt. 1

Since I'm making a presentation for work, i figured I might as well write it out.

In this part I'll explain my viewpoint, and point out to Shuhari, vertical slices, kata, and the Cynefin framework as helpful tools for figuring out where you are.

In next three parts I'll explain what I think it means to be a good junior, experienced, and senior developer.

About me and the purpose of this talk/article

I don't especially care to impress you and establish my credibility in detail. I'm not the wisest coolest fastest developer you've ever seen, but I've been programming for ~35 years and spent most of my adult life as a professional software developer and architect. I never sought leadership or management positions, but I've been involved in hiring, onboarding, documentation, etc.

The purpose of this is to give you something to think about, to gain some clarity about how to progress. This is not a technical tutorial or life hack or your therapy session.

Classic warning labels

I’m not your dad, it’s your life, I won't tell you what to do with your career.

This is not a criticism of any of you, and please don’t come at me with “this doesn’t apply to me actually”. I will likely say something like "senior dev should know this" and you might be a senior and not know it, it's fine. This is not an appraisal, I'm not your boss, your happiness doesn't depend on me.

And even when I use the labels "junior", "experienced" and "senior" developer, I see zero benefit in assigning you three rigid categories. We're all dumb in our own ways, we're all clever and wise in our own ways.

Let's begin.

Shuhari

https://en.wikipedia.org/wiki/Shuhari

Shu-ha-ri (守破離) is a way of viewing mastery of any skill as three stages. Instead of using the more typical western idea of having "experts" who are people who just Know a lot, it instead focuses on how you interact with the skill.

In very simplified terms, it's obeying the rules and respecting the tradition (Shu), then evolving the existing rules by breaking them bit by bit (Ha), and eventually detaching yourself from the usual wisdom and rules and just vibing (Ri).

A simple way to remember the Shuhari stages - follow the rules, break the rules, transcend the rules.

Another way to look at it is mimicking others (Shu), taking a step back and understanding context (Ha) and having a global perspective (Ri).

For example, I've made 1500-2000 pancakes over the past 13 years. I started by following the existing recipe and measures (Shu). I started trying different variations and ingredients from different recommendations (still Shu).

Eventually I started breaking the traditional recipes by adding ingredients that didn't seem expected (Ha) and improvising more.

I'm not confident I'd say I reached the Ri stage, because I still use the same basic ingredients since I have a relatively limited, desired outcome. I'd argue to really be in Ri level of mastery I'd have to have a MacGyver-like flexibility when it comes to ingredients.

At that's fine. Not everyone needs to be a guru.

The important thing is - someone at Ri level of making pancakes isn't just making Shu level pancakes very very fast.

A "Shu" developer repeats what they learned in school, copy pastes from Stack Overflow, follows advice of senior developers, makes simple CRUD REST endpoints.

A "Ha" developer can improve on existing tooling or workflow, remove more complex technical debt and knows when to have exceptions to common rules.

A "Ri" developer is someone who invents workflows, architecture, enterprise patterns, combines tech stack in creative ways, and doesn't necessarily follow hype.

It should be noted that in real world, developers don't have infinite freedom because of practical considerations - audits, legal requirements, ISO certifications, Jira, limitations in your employees' know-how, etc. I can't just develop something in COBOL and then deploy it outside of a Kubernetes cluster just cause it would be a cool way to solve a problem, it needs to fit into the company goals and needs and policies.

This, sadly, also means that a company can restrict your growth in some ways. It doesn't mean you can't grow, but you can't grow in any possible way imaginable. Choose your battles, etc.

Why is this useful?

It might give you a better framework for analyzing your skill set than "junior" "intermediate" "expert". Shuhari isn't about the amount of your knowledge, it's about how you practice your skill and what is your current approach to learning.

And again - being on Shu level doesn't mean your bad / evil / stupid / incompetent / slow / dumb / etc.

Kata

This is not a new or difficult concept. Kata are the unit tests of your skills. The best way to learn is in small pieces. Sometimes all you need to do is write a few lines of code in REPL.

ADHD and others

This is not a medical advice, but keep in mind that you might prefer different learning style than others. Some people like to RTFM. Some want to dive in and try it on their own. You'll have to balance finding and using the style you prefer, but also remembering the limitations of each method. Watching youtube doesn't give you actual experience. Reading the manual doesn't help you remember everything. Trial and error programming won't alert you to potential pitfalls the code will have in edge cases.

The most effective method is, always was, and always will be having a mentor.

Remember to take breaks. Fresh air, clean water, healthy, varied diet, regular movement and exercise. With both diet and exercise, adopt an additive mindset - sure you might be eating a greasy frozen pizza, but if you add some spinach, rucola, tomatoes, peppers on top of it, you're eating _some_ vegetables. If you do only 1 push-up per day, it's infinitely more than 0 pushups.

If blaming or hating yourself for not doing enough would work, it would have worked by now.

Medication might help some. To get diagnosed with ADHD as an adult in Estonia, you must document that it's affecting your life, fulfill the diagnostic criteria, and fork out 250~350 euro for a cognitive assessment. Don't bother with state psychiatrists.

Some over the counter supplements that might or might not help: Vitamin D, Omega-3, Lecithin, Magnesium L-Threonate, Ginkgo Biloba. Caffeine stimulates your brain indiscriminately and might make it harder to concentrate, and also builds up tolerance.

Cynefin

See more at https://en.wikipedia.org/wiki/Cynefin_framework 

Cynefin (Welsh for 'habitat', pronounced like if you take the name Kevin and make it keh-nev-in... i think) is a framework usually used for crisis management and decision making. However, you can use it to aid your learning, to help make sense of situations like production incidents, or when refining tasks during planning meetings.

One use is to look at the 5 domains and figuring out which of them are you comfortable with, and where is your current task located. The names might not be what they seem at first though. They don't represent how long will a task take.

Let's start from bottom right and then move counter-clockwise.

(1) The bottom-right domain is called Clear or Obvious or Simple or Known - it's easy to think of it as tasks like CRUD, BO page with pagination. Generally something that can be easily unit tested.

However, even more complex tasks like placing an order - where there's a lot to keep in mind, many branched pathways, legal requirements, asynchronous calls, etc, something you’d cover with a bunch of integration tests - is still considered “clear” in this framework. If there are defined rules leading to defined results, it's "Clear".

(2) Top right corner is Complicated or Knowable - e.g. an incident in production - a bug that we haven’t found, or an unidentified performance issue. The approach for these is “Sense - analyze - respond” or maybe for tasks that are not burning, “have a meetings and discuss and split the tasks". If you're feeling overwhelmed by a task, it's maybe because it's in the Complicated domain, and you need to find a way to move it to the Clear domain.

(3) Complex domain - investigating an incident where you don’t know what’s wrong and what causes it (untestable, impossible to replicate). Most likely, this is a production incident when you don't even know what's going on. Instead of looking at a dashboard and seeing "oh this endpoint is slow", it's something like "something is slow sometimes but we don't know what caused it and what is a side effect". In this domain, you would probably add more logging, create new Grafana graphs, dive deep into Kibana logs, etc.

Definitely not a domain that should be a part of feature development, unless you're way out of your depth and completely misunderstood how a given technology works.

(4) Chaos domain is not a good place to be. The cause and effect are unclear, e.g. fighting off a hacking attack. It's never happened before, there are no best practices, no playbook, best action is any action. "Have you tried turning it off and on again" style approach, but it might work on some occasions - it's better than nothing. Generally you want to move out of this domain asap.

Example 1: Improving a performance by adding an SQL index can be Simple/Clear/Obvious, but adding redis caching with invalidation to endpoints can be Complicated, if you don't know until you try, and it can be Complex, if you have cache that isn't invalidated immediately, and the impact of having an outdated cache and inconsistent data might be difficult to understand.

If you mess it up and wrong data starts showing to wrong customers, you might feel like it's chaotic because it's stressful, but you're really in Simple or Complicated situation, because you either you know you messed up the caching rules, or you don't know exactly, but have a way to measure it and find out.

(5) Confusion in the middle of the illustration - when you don’t know which one you have, best to split the problem and try to assign parts into different 4 domains.

Remember that for any situation, the domains are individual - a non-programmer can see BO acting weird (Chaotic domain or Confusion), junior dev can see slowness without an obvious cause (Complicated domain) DBA can see a missing index (Simple).

Possibly the most important thing to remember is that you can keep moving the problem between the domains.

Example 2:

implementing an existing compression algorithm is Simple.

developing a new disassembly tool, DRM, or compression is Complicated (trial and error to work around more and more tricks)

developing an algorithm that does open heart surgeries is impossible Complex

Trying to crack a brand new cipher is Chaotic because you don't know what's the content, what's the cipher, what information is there in what format, how many layers of compression, encryption and encoding are there

Example 3:

developing an illegal, unlicensed Tetris™️ prototype is simple, and there are plenty of tutorials available

developing a PvP multiplayer game is Complicated, because you'll have to measure many different unpredictable situations, strategies, and combinations to balance it

developing an MMORPG like EVE Online is Complex because there's no easy, orderly way to have 5'000 players shoot lasers at each other for 12 hours.

developing any game is Chaotic if you're an overconfident noob

Example 4:

making a fake sportsbook website without any real money is Simple

making a real sportsbook website with real money and wallet and 3rd party odds is Simple, even if it will take months

managing odds is both Complicated and Complex

making good UI for both FO and BO is Complex

making a sportsbook website that performs well under a very high load with very fast resolving is Complex because there is never any realistic load testing tool

Example 5:

fixing a bug in logic in a feature that's otherwise behaving correctly and has clean code is usually Simple

fixing a bug in a horrible spaghetti code is Complicated

fixing a bug in an OS kernel on some specific hardware that exhibits undocumented behavior is Complex

trying to fix a software bug when you actually have physical memory corruption is Chaotic

Figuring out how to use Cynefin is up to you. If nothing else, remember to try to take a step back, have a fresh look at a task that's stumping you, and figuring out why isn't the task "Simple". Usually it's one of the three - either you're lacking some technical knowledge (read the manual; Complicated -> Simple), or you're not sure how exactly it is used in our company (ask questions; Complex -> Complicated -> Simple), or you're overwhelmed by a task that's otherwise in your capacity (split the task; Complicated -> Simple).

[govindhtech]

What is Argo CD? And When Was Argo CD Established?

What Is Argo CD?

Argo CD is declarative Kubernetes GitOps continuous delivery.

In DevOps, ArgoCD is a Continuous Delivery (CD) technology that has become well-liked for delivering applications to Kubernetes. It is based on the GitOps deployment methodology.

When was Argo CD Established?

Argo CD was created at Intuit and made publicly available following Applatix’s 2018 acquisition by Intuit. The founding developers of Applatix, Hong Wang, Jesse Suen, and Alexander Matyushentsev, made the Argo project open-source in 2017.

Why Argo CD?

Declarative and version-controlled application definitions, configurations, and environments are ideal. Automated, auditable, and easily comprehensible application deployment and lifecycle management are essential.

Getting Started

Quick Start

kubectl create namespace argocd kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

For some features, more user-friendly documentation is offered. Refer to the upgrade guide if you want to upgrade your Argo CD. Those interested in creating third-party connectors can access developer-oriented resources.

How it works

Argo CD defines the intended application state by employing Git repositories as the source of truth, in accordance with the GitOps pattern. There are various approaches to specify Kubernetes manifests:

Applications for Customization

Helm charts

JSONNET files

Simple YAML/JSON manifest directory

Any custom configuration management tool that is set up as a plugin

The deployment of the intended application states in the designated target settings is automated by Argo CD. Deployments of applications can monitor changes to branches, tags, or pinned to a particular manifest version at a Git commit.

Architecture

The implementation of Argo CD is a Kubernetes controller that continually observes active apps and contrasts their present, live state with the target state (as defined in the Git repository). Out Of Sync is the term used to describe a deployed application whose live state differs from the target state. In addition to reporting and visualizing the differences, Argo CD offers the ability to manually or automatically sync the current state back to the intended goal state. The designated target environments can automatically apply and reflect any changes made to the intended target state in the Git repository.

Components

API Server

The Web UI, CLI, and CI/CD systems use the API, which is exposed by the gRPC/REST server. Its duties include the following:

Status reporting and application management

Launching application functions (such as rollback, sync, and user-defined actions)

Cluster credential management and repository (k8s secrets)

RBAC enforcement

Authentication, and auth delegation to outside identity providers

Git webhook event listener/forwarder

Repository Server

An internal service called the repository server keeps a local cache of the Git repository containing the application manifests. When given the following inputs, it is in charge of creating and returning the Kubernetes manifests:

URL of the repository

Revision (tag, branch, commit)

Path of the application

Template-specific configurations: helm values.yaml, parameters

A Kubernetes controller known as the application controller keeps an eye on all active apps and contrasts their actual, live state with the intended target state as defined in the repository. When it identifies an Out Of Sync application state, it may take remedial action. It is in charge of calling any user-specified hooks for lifecycle events (Sync, PostSync, and PreSync).

Features

Applications are automatically deployed to designated target environments.

Multiple configuration management/templating tools (Kustomize, Helm, Jsonnet, and plain-YAML) are supported.

Capacity to oversee and implement across several clusters

Integration of SSO (OIDC, OAuth2, LDAP, SAML 2.0, Microsoft, LinkedIn, GitHub, GitLab)

RBAC and multi-tenancy authorization policies

Rollback/Roll-anywhere to any Git repository-committed application configuration

Analysis of the application resources’ health state

Automated visualization and detection of configuration drift

Applications can be synced manually or automatically to their desired state.

Web user interface that shows program activity in real time

CLI for CI integration and automation

Integration of webhooks (GitHub, BitBucket, GitLab)

Tokens of access for automation

Hooks for PreSync, Sync, and PostSync to facilitate intricate application rollouts (such as canary and blue/green upgrades)

Application event and API call audit trails

Prometheus measurements

To override helm parameters in Git, use parameter overrides.

Read more on Govindhtech.com

[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.

[stevediazblog]

Best DevOps Certifications for Elevating Your Skills

DevOps is a rapidly growing area that bridging the gap between IT and software development operations, encouraging the culture which is constantly integrating, automated and quick delivery. If you're a novice looking to start your career in the field or a seasoned professional seeking to increase your knowledge by earning the DevOps certification can greatly improve your job prospects.

This article will discuss the most beneficial DevOps certifications to be awarded in 2025 which will aid you in enhancing your capabilities and remain ahead in the ever-changing tech sector.

Why Get a DevOps Certification?

Certifications in DevOps demonstrate your expertise and experience of automation, cloud computing pipelines, CI/CD pipelines and infrastructure in code and many more. The reasons why getting a certification is useful:

Improved Career Opportunities : Certified DevOps professionals are highly sought after as top companies seek experienced engineers.

Higher Salary - Potential : The Accredited DevOps engineers earn considerably higher salaries than professionals who are not certified.

A better set of skills : Certifications can assist you in mastering tools such as Kubernetes, Docker, Ansible, Jenkins, and Terraform.

Higher job security : With the swift acceptance of DevOps methods, certified professionals have a better position in the market.

Top DevOps Certifications in 2025

1. AWS Certified DevOps Engineer – Professional

Best for: Cloud DevOps Engineers

Skills Covered: AWS automation, CI/CD pipelines, logging, and monitoring

Cost: $300

Why Choose This Certification? AWS is a dominant cloud provider, and this certification validates your expertise in implementing DevOps practices within AWS environments. It's ideal for those working with AWS services like EC2, Lambda, CloudFormation, and CodePipeline.

2. Microsoft Certified: DevOps Engineer Expert

Best for: Azure DevOps Professionals

Skills Covered: Azure DevOps, CI/CD, security, and monitoring

Cost: $165

Why Choose This Certification? If you're working in Microsoft Azure environments, this certification is a must. It covers using Azure DevOps Services, infrastructure as code with ARM templates, and managing Kubernetes deployments in Azure.

3. Google Cloud Professional DevOps Engineer

Best for: Google Cloud DevOps Engineers

Skills Covered: GCP infrastructure, CI/CD, monitoring, and incident response

Cost: $200

Why Choose This Certification? Google Cloud is growing in popularity, and this certification helps DevOps professionals validate their skills in site reliability engineering (SRE) and continuous delivery using GCP.

4. Kubernetes Certifications (CKA & CKAD)

Best for: Kubernetes Administrators & Developers

Skills Covered: Kubernetes cluster management, networking, and security

Cost: $395 each

Why Choose These Certifications? Kubernetes is the backbone of container orchestration, making these certifications essential for DevOps professionals dealing with cloud-native applications and containerized deployments.

CKA (Certified Kubernetes Administrator): Best for managing Kubernetes clusters.

CKAD (Certified Kubernetes Application Developer): Best for developers deploying apps in Kubernetes.

5. Docker Certified Associate (DCA)

Best for: Containerization Experts

Skills Covered: Docker containers, Swarm, storage, and networking

Cost: $195

Why Choose This Certification? Docker is a key component of DevOps workflows. The DCA certification helps you gain expertise in containerized applications, orchestration, and security best practices.

6. HashiCorp Certified Terraform Associate

Best for: Infrastructure as Code (IaC) Professionals

Skills Covered: Terraform, infrastructure automation, and cloud deployments

Cost: $70

Why Choose This Certification? Terraform is the leading Infrastructure as Code (IaC) tool. This certification proves your ability to manage cloud infrastructure efficiently across AWS, Azure, and GCP.

7. Red Hat Certified Specialist in Ansible Automation

Best for: Automation Engineers

Skills Covered: Ansible playbooks, configuration management, and security

Cost: $400

Why Choose This Certification? If you work with IT automation and configuration management, this certification validates your Ansible skills, making you a valuable asset for enterprises looking to streamline operations.

8. DevOps Institute Certifications (DASM, DOFD, and DOL)

Best for: DevOps Leadership & Fundamentals

Skills Covered: DevOps culture, processes, and automation best practices

Cost: Varies

Why Choose These Certifications? The DevOps Institute offers various certifications focusing on foundational DevOps knowledge, agile methodologies, and leadership skills, making them ideal for beginners and managers.

How to Choose the Right DevOps Certification?

Consider the following factors when selecting a Devops certification course online :

Your Experience Level: Beginners may start with Docker, Terraform, or DevOps Institute certifications, while experienced professionals can pursue AWS, Azure, or Kubernetes certifications.

Your Career Goals: If you work with a specific cloud provider, choose AWS, Azure, or GCP certifications. For automation, go with Terraform or Ansible.

Industry Demand: Certifications like AWS DevOps Engineer, Kubernetes, and Terraform have high industry demand and can boost your career.

Cost & Time Commitment: Some certifications require hands-on experience, practice labs, and exams, so choose one that fits your schedule and budget.

Final Thoughts

Certifications in DevOps can increase your technical knowledge and improve your chances of getting a job and enhance the amount you earn. No matter if you're looking to specialise in cloud-based platforms, automation, and containerization, you can find an option specifically designed for your specific career with Cloud Computing Certification Courses.

If you can earn at least one of these most prestigious DevOps-related certifications in 2025 You can establish yourself as a professional with a solid background in an industry that is rapidly changing.

[hawkstack]

Managing Stateful Applications with OpenShift Containers

In today's cloud-native world, containers have revolutionized the way we develop, deploy, and manage applications. However, when it comes to stateful applications—those that require persistent data storage—things get a bit more complex. OpenShift, a leading Kubernetes-based platform, provides robust tools and features to effectively manage stateful applications. In this article, we’ll explore how to manage stateful applications using OpenShift Containers, best practices, and key considerations for ensuring data consistency and availability.

What Are Stateful Applications?

Stateful applications are those that require persistent data storage to maintain state across sessions. Unlike stateless applications, which don’t store user or session data, stateful apps need consistent data access. Examples include databases, message queues, and content management systems.

In a containerized environment, managing stateful applications can be challenging due to the ephemeral nature of containers. OpenShift addresses these challenges with advanced storage and orchestration solutions.

Challenges of Managing Stateful Applications in Containers

Data Persistence: Containers are inherently ephemeral, meaning data stored locally is lost when a container restarts or scales down.

Scaling and High Availability: Ensuring data consistency across multiple instances is complex.

Backup and Recovery: Stateful applications require robust backup and disaster recovery mechanisms.

Storage Provisioning and Management: Efficient storage allocation and management are crucial to maintain performance and cost-efficiency.

How OpenShift Handles Stateful Applications

OpenShift extends Kubernetes' capabilities by offering enhanced tools for managing stateful applications, including:

1. Persistent Volume (PV) and Persistent Volume Claim (PVC)

OpenShift decouples storage from containers using PVs and PVCs.

Persistent Volumes (PVs): Storage resources provisioned by an administrator.

Persistent Volume Claims (PVCs): Requests for storage made by developers or applications. This separation allows for flexible storage management, making it easier to scale stateful applications.

2. StatefulSets

OpenShift uses StatefulSets to manage stateful applications. StatefulSets maintain a unique identity and persistent storage for each pod, ensuring:

Consistent network identifiers

Ordered deployment and scaling

Stable persistent storage

3. OpenShift Container Storage (OCS)

OpenShift Container Storage provides a software-defined storage solution that integrates seamlessly with OpenShift clusters, offering:

Dynamic Provisioning: Automatically provisions storage based on PVCs.

Data Replication: Ensures high availability and disaster recovery.

Multi-Cloud Support: Enables hybrid and multi-cloud deployments.

Best Practices for Managing Stateful Applications

Use StatefulSets for Stateful Workloads StatefulSets provide ordered deployment, scaling, and consistent storage, making them ideal for databases and messaging queues.

Leverage OpenShift Container Storage (OCS) OCS provides dynamic provisioning, replication, and multi-cloud support, ensuring data availability and consistency.

Data Backup and Disaster Recovery Implement robust backup and disaster recovery strategies using tools like Velero, which integrates with OpenShift for data protection.

Optimize Storage Costs Utilize OpenShift's storage classes to efficiently allocate and manage storage resources, optimizing costs.

Monitor and Scale Proactively Use OpenShift's monitoring tools to proactively monitor resource usage and scale stateful applications as needed.

Example: Deploying a Stateful Application on OpenShift

Here’s a simple example of deploying a MySQL database using StatefulSets and Persistent Volumes on OpenShift:

apiVersion: v1

kind: PersistentVolumeClaim

metadata:

  name: mysql-pvc

spec:

  accessModes:

    - ReadWriteOnce

  resources:

    requests:

      storage: 10Gi

---

apiVersion: apps/v1

kind: StatefulSet

metadata:

  name: mysql

spec:

  serviceName: "mysql"

  replicas: 1

  selector:

    matchLabels:

      app: mysql

  template:

    metadata:

      labels:

        app: mysql

    spec:

      containers:

      - name: mysql

        image: mysql:5.7

        ports:

        - containerPort: 3306

          name: mysql

        volumeMounts:

        - name: mysql-data

          mountPath: /var/lib/mysql

  volumeClaimTemplates:

  - metadata:

      name: mysql-data

    spec:

      accessModes: [ "ReadWriteOnce" ]

      resources:

        requests:

          storage: 10Gi

This YAML configuration:

Creates a Persistent Volume Claim for storage.

Defines a StatefulSet to deploy MySQL with consistent network identity and persistent storage.

Advantages of Using OpenShift for Stateful Applications

High Availability and Scalability: OpenShift ensures high availability and seamless scaling for stateful applications.

Multi-Cloud Flexibility: Deploy stateful applications across hybrid and multi-cloud environments with ease.

Enhanced Security and Compliance: OpenShift provides built-in security features, ensuring compliance with enterprise standards.

Conclusion

Managing stateful applications in a containerized environment requires strategic planning and robust tools. OpenShift provides a powerful platform with StatefulSets, Persistent Volumes, and OpenShift Container Storage (OCS) to efficiently manage stateful applications.

By leveraging OpenShift's advanced features and following best practices, organizations can ensure high availability, data consistency, and cost-effective storage management for stateful applications.

Want to Learn More?

At HawkStack Technologies, we specialize in helping enterprises implement and manage OpenShift solutions tailored to their needs. Contact us today to learn how we can assist you in deploying and scaling stateful applications with OpenShift! For more details click www.hawkstack.com 

[santecky2]

What Oracle Skills Are Currently In High Demand?

Introduction

Oracle technologies dominate the enterprise software landscape, with businesses worldwide relying on Oracle databases, cloud solutions, and applications to manage their data and operations. As a result, professionals with Oracle expertise are in high demand. Whether you are an aspiring Oracle professional or looking to upgrade your skills, understanding the most sought-after Oracle competencies can enhance your career prospects. Here are the top Oracle skills currently in high demand.

Oracle Database Administration (DBA)

Oracle Database Administrators (DBAs) are crucial in managing and maintaining Oracle databases. Unlock the world of database management with our Oracle Training in Chennai at Infycle Technologies. Organizations seek professionals to ensure optimal database performance, security, and availability. Key skills in demand include:

Database installation, configuration, and upgrades

Performance tuning and optimization

Backup and recovery strategies using RMAN

High-availability solutions such as Oracle RAC (Real Application Clusters)

Security management, including user access control and encryption

SQL and PL/SQL programming for database development

Oracle Cloud Infrastructure (OCI)

With businesses rapidly moving to the cloud, Oracle Cloud Infrastructure (OCI) has become a preferred choice for many enterprises. Professionals skilled in OCI can help organizations deploy, manage, and optimize cloud environments. Key areas of expertise include:

Oracle Cloud Architecture and Networking

OCI Compute, Storage, and Database services

Identity and Access Management (IAM)

Oracle Kubernetes Engine (OKE)

Security Best Practices and Compliance in Cloud Environments

Migration strategies from on-premises databases to OCI

Oracle SQL And PL/SQL Development

Oracle SQL and PL/SQL are fundamental database developers, analysts, and administrators skills. Companies need professionals who can:

Write efficient SQL queries for data retrieval & manipulation

Develop PL/SQL procedures, triggers, and packages

Optimize database performance with indexing and query tuning

Work with advanced SQL analytics functions and data modelling

Implement automation using PL/SQL scripts

Oracle ERP And E-Business Suite (EBS)

Enterprise Resource Planning (ERP) solutions from Oracle, such as Oracle E-Business Suite (EBS) and Oracle Fusion Cloud ERP, are widely used by organizations to manage business operations. Professionals with rising experience in these areas are highly sought after. Essential skills include:

ERP implementation and customization

Oracle Financials, HRMS, and Supply Chain modules

Oracle Workflow and Business Process Management

Reporting and analytics using Oracle BI Publisher

Integration with third-party applications

Oracle Fusion Middleware

Oracle Fusion Middleware is a comprehensive software suite that facilitates application integration, business process automation, and security. Professionals with experience in:

Oracle WebLogic Server administration

Oracle SOA Suite (Service-Oriented Architecture)

Oracle Identity and Access Management (IAM)

Oracle Data Integration and ETL tools

The job market highly values Java EE and Oracle Application Development Framework (ADF).

Oracle BI (Business Intelligence) And Analytics

Data-driven decision-making is critical for modern businesses, and Oracle Business Intelligence (BI) solutions help organizations derive insights from their data. In-demand skills include:

Oracle BI Enterprise Edition (OBIEE)

Oracle Analytics Cloud (OAC)

Data warehousing concepts and ETL processes

Oracle Data Visualization and Dashboarding

Advanced analytics using machine learning and AI tools within Oracle BI

Oracle Exadata And Performance Tuning

Oracle Exadata is a high-performance engineered system designed for large-scale database workloads. Professionals skilled in Oracle Exadata and performance tuning are in great demand. Essential competencies include:

Exadata architecture and configuration

Smart Flash Cache and Hybrid Columnar Compression (HCC)

Exadata performance tuning techniques

Storage indexing and SQL query optimization

Integration with Oracle Cloud for hybrid cloud environments

Oracle Security And Compliance

Organizations need Oracle professionals to ensure database and application security with increasing cybersecurity threats. Key security-related Oracle skills include:

Oracle Data Safe and Database Security Best Practices

Oracle Audit Vault and Database Firewall

Role-based access control (RBAC) implementation

Encryption and Data Masking techniques

Compliance with regulations like GDPR and HIPAA

Oracle DevOps And Automation

DevOps practices have become essential for modern software development and IT operations. Enhance your journey toward a successful career in software development with Infycle Technologies, the Best Software Training Institute in Chennai. Oracle professionals with DevOps expertise are highly valued for their ability to automate processes and ensure continuous integration and deployment (CI/CD). Relevant skills include:

Oracle Cloud DevOps tools and automation frameworks

Terraform for Oracle Cloud Infrastructure provisioning

CI/CD pipeline implementation using Jenkins and GitHub Actions

Infrastructure as Code (IaC) practices with Oracle Cloud

Monitoring and logging using Oracle Cloud Observability tools

Oracle AI And Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are transforming businesses' operations, and Oracle has integrated AI/ML capabilities into its products. Professionals with expertise in:

Oracle Machine Learning (OML) for databases

AI-driven analytics in Oracle Analytics Cloud

Chatbots and AI-powered automation using Oracle Digital Assistant

Data Science and Big Data processing with Oracle Cloud are highly demanding for data-driven decision-making roles.

Conclusion

The demand for Oracle professionals grows as businesses leverage Oracle's powerful database, cloud, and enterprise solutions. Whether you are a database administrator, cloud engineer, developer, or security expert, acquiring the right Oracle skills can enhance your career opportunities and keep you ahead in the competitive job market. You can position yourself as a valuable asset in the IT industry by focusing on high-demand skills such as Oracle Cloud Infrastructure, database administration, ERP solutions, and AI/ML integration. If you want to become an expert in Oracle technologies, consider enrolling in Oracle certification programs, attending workshops, and gaining hands-on experience to strengthen your skill set and stay ahead in the industry.

[learning-code-ficusoft]

How to Use AWS Code Pipeline for Continuous Delivery

Introduction to AWS Code Pipeline: Overview of CI/CD and How It Works

1. What is AWS Code Pipeline?

AWS Code Pipeline is a fully managed continuous integration and continuous delivery (CI/CD) service that automates software release processes. It enables developers to build, test, and deploy applications rapidly with minimal manual intervention.

2. What is CI/CD?

CI/CD stands for:

Continuous Integration (CI): Automates the process of integrating code changes from multiple contributors.

Continuous Delivery (CD): Ensures that code is always in a deployable state and can be released automatically.

Benefits of CI/CD with AWS Code Pipeline

✅ Faster Deployments: Automates the release process, reducing time to market. ✅ Consistent & Reliable: Eliminates manual errors by enforcing a consistent deployment workflow. ✅ Scalability: Easily integrates with AWS services like Code Build, CodeDeploy, Lambda, and more. ✅ Customizable: Allows integration with third-party tools like GitHub, Jenkins, and Bitbucket.

3. How AWS Code Pipeline Works

AWS Code Pipeline follows a pipeline-based workflow, consisting of three key stages:

1️⃣ Source Stage (Code Repository)

The pipeline starts when new code is committed.

Can be triggered by AWS Code Commit, GitHub, Bitbucket, or an S3 bucket.

2️⃣ Build & Test Stage (CI Process)

Uses AWS Code Build or third-party tools to compile code, run tests, and package applications.

Ensures the application is error-free before deployment.

3️⃣ Deployment Stage (CD Process)

Uses AWS Code Deploy, ECS, Lambda, or Elastic Beanstalk to deploy the application.

Supports blue/green and rolling deployments for zero-downtime updates.

4. Example: CI/CD Workflow with AWS Code Pipeline

🔹 Step 1: Developer Pushes Code

A developer pushes code to a GitHub repository or AWS Code Commit.

🔹 Step 2: AWS Code Pipeline Detects the Change

The pipeline is triggered automatically by a new commit.

🔹 Step 3: Build & Test the Code

AWS Code Build compiles the application, runs unit tests, and packages the output.

🔹 Step 4: Deploy to AWS Services

AWS Code Deploy or ECS deploys the application to EC2, Lambda, or Kubernetes clusters.

🔹 Step 5: Monitor and Optimize

AWS Cloud Watch logs and AWS X-Ray provide visibility into the CI/CD pipeline’s performance.

5. Conclusion

AWS Code Pipeline automates software delivery by integrating source control, build, test, and deployment into a seamless workflow. With AWS-managed services, it helps teams improve efficiency, reduce errors, and deploy applications faster.

WEBSITE: https://www.ficusoft.in/devops-training-in-chennai/

[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…

[azureai102engineer]

Azure AI Engineer Training | Azure AI Engineer Online

How Azure Blob Storage Integrates with AI and Machine Learning Models

Introduction

Azure Blob Storage is a scalable, secure, and cost-effective cloud storage solution offered by Microsoft Azure. It is widely used for storing unstructured data such as images, videos, documents, and logs. Its seamless integration with AI and machine learning (ML) models makes it a powerful tool for businesses and developers aiming to build intelligent applications. This article explores how Azure Blob Storage integrates with AI and ML models to enable efficient data management, processing, and analytics. Microsoft Azure AI Engineer Training

Why Use Azure Blob Storage for AI and ML?

Machine learning models require vast amounts of data for training and inference. Azure Blob Storage provides:

Scalability: Handles large datasets efficiently without performance degradation.

Security: Built-in security features, including role-based access control (RBAC) and encryption.

Cost-effectiveness: Offers different storage tiers (hot, cool, and archive) to optimize costs.

Integration Capabilities: Works seamlessly with Azure AI services, ML tools, and data pipelines.

Integration of Azure Blob Storage with AI and ML

1. Data Storage and Management

Azure Blob Storage serves as a central repository for AI and ML datasets. It supports various file formats such as CSV, JSON, Parquet, and image files, which are crucial for training deep learning models. The ability to store raw and processed data makes it a vital component in AI workflows. Azure AI Engineer Online Training

2. Data Ingestion and Preprocessing

AI models require clean and structured data. Azure provides various tools to automate data ingestion and preprocessing:

Azure Data Factory: Allows scheduled and automated data movement from different sources into Azure Blob Storage.

Azure Databricks: Helps preprocess large datasets before feeding them into ML models.

Azure Functions: Facilitates event-driven data transformation before storage.

3. Training Machine Learning Models

Once the data is stored in Azure Blob Storage, it can be accessed by ML frameworks for training:

Azure Machine Learning (Azure ML): Directly integrates with Blob Storage to access training data.

PyTorch and TensorFlow: Can fetch and preprocess data stored in Azure Blob Storage.

Azure Kubernetes Service (AKS): Supports distributed ML training on GPU-enabled clusters.

4. Model Deployment and Inference

Azure Blob Storage enables efficient model deployment and inference by storing trained models and inference data: Azure AI Engineer Training

Azure ML Endpoints: Deploy trained models for real-time or batch inference.

Azure Functions & Logic Apps: Automate model inference by triggering workflows when new data is uploaded.

Azure Cognitive Services: Uses data from Blob Storage for AI-driven applications like vision recognition and natural language processing (NLP).

5. Real-time Analytics and Monitoring

AI models require continuous monitoring and improvement. Azure Blob Storage works with:

Azure Synapse Analytics: For large-scale data analytics and AI model insights.

Power BI: To visualize trends and model performance metrics.

Azure Monitor and Log Analytics: Tracks model predictions and identifies anomalies.

Use Cases of Azure Blob Storage in AI and ML

Image Recognition: Stores millions of labeled images for training computer vision models.

Speech Processing: Stores audio datasets for training speech-to-text AI models.

Healthcare AI: Stores medical imaging data for AI-powered diagnostics.

Financial Fraud Detection: Stores historical transaction data for training anomaly detection models. AI 102 Certification

Conclusion

Azure Blob Storage is critical in AI and ML workflows by providing scalable, secure, and cost-efficient data storage. Its seamless integration with Azure AI services, ML frameworks, and analytics tools enables businesses to build and deploy intelligent applications efficiently. By leveraging Azure Blob Storage, organizations can streamline data handling and enhance AI-driven decision-making processes.

For More Information about Azure AI Engineer Certification Contact Call/WhatsApp: +91-7032290546

Visit: https://www.visualpath.in/azure-ai-online-training.html

[zetsofft]

Hybrid Cloud mit Azure Stack HCI

Azure Stack HCI ist ein wichtiger Bestandteil einer Hybrid Cloud Strategie. Durch die Kombination von lokalen und Cloud-Ressourcen können Sie die Vorteile beider Welten nutzen.

Vorteile einer Hybrid Cloud:

Optimale Auslastung: Verschieben Sie Workloads zwischen lokalen und Cloud-Ressourcen, um Kosten zu optimieren.

Disaster Recovery: Schützen Sie Ihre Daten durch Replikation in die Cloud.

Innovation: Nutzen Sie Cloud-Dienste für neue Anwendungen und Geschäftsmodelle.

Hybrid Cloud Beratung in München

Network4you unterstützt Sie bei der Entwicklung Ihrer Hybrid Cloud Strategie und der Umsetzung mit Azure Stack HCI. Wir helfen Ihnen, die richtigen Workloads für die Cloud und für das lokale Rechenzentrum auszuwählen.

Azure Arc – Verwalten Sie Ihre Ressourcen überall

Azure Arc ermöglicht Ihnen die Verwaltung Ihrer Ressourcen über verschiedene Plattformen hinweg, einschließlich Kubernetes-Cluster, Server und IoT-Geräte. Mit Azure Arc können Sie Ihre Ressourcen zentralisieren und einheitliche Richtlinien anwenden.

Vorteile von Azure Arc:

Hybrid Cloud-Management: Vereinfachen Sie die Verwaltung Ihrer Hybrid Cloud-Umgebung.

Einheitliche Verwaltung: Verwalten Sie Ihre Ressourcen über eine zentrale Plattform.

Compliance: Stellen Sie die Einhaltung von Richtlinien sicher.

Azure Beratung München – Ihr Partner für Erfolg

Systemhaus München ist Ihr kompetenter Partner für Microsoft Azure in München. Wir bieten Ihnen umfassende Beratungsleistungen, um die optimalen Azure-Dienste für Ihr Unternehmen zu identifizieren. Darüber hinaus unterstützen wir Sie bei der Implementierung und Migration Ihrer IT-Infrastruktur in die Azure Cloud.

Unser Team aus erfahrenen Azure-Experten steht Ihnen zur Seite, um Ihre individuellen Anforderungen zu verstehen und maßgeschneiderte Lösungen zu entwickeln. Von der Strategieentwicklung bis zur Umsetzung und Betreuung – wir begleiten Sie auf Ihrem Weg in die Cloud.

Hybrid Cloud mit Azure Stack HCI

[datascienceblog12]

Top Cloud Computing Certification Courses To Boost Your Skills In 2025

Cloud computing plays a critical role in today’s digital world. The industries and cloud technologies go hand-in-hand. These technologies help in streaming processes, fastening the software delivery systems, etc. Regardless of your job position i.e. cloud engineer, devOps professional, IT administrator, the cloud computing certification courses give opportunity to every individual. It provides a quality learning experience with in-depth theoretical exposure. This article lists the best courses that are often regarded as the most reputable in 2025.

AWS Certified: Solutions Architect Associate- Amazon Web Services is among the most prestigious certifications in the cloud computing industry. This certification validates your mastery in conceiving scalable and cost-efficient cloud resolutions on AWS. It covers topics such as AWS architectural best practices, security & compliance, AWS storage, networking, and computing services. This also helps in cost optimization. It is ideal for IT experts transitioning to cloud computing, cloud engineers, and architects. 

Microsoft Certified: Azure Solutions Architect Expert- when it comes to the cloud service provider then it tops regarding the efficacy. This credential is indispensable for specialists operating in Microsoft-based cloud environments. It validates expertise in devising and executing solutions on Microsoft Azure. The key topics include executing security and identity solutions. It encourages data storage and networking solutions, business continuity strategies, monitoring, and optimizing Azure solutions. This certification is well-suited for cloud solution architects and IT professionals with Azure experience. 

Google Cloud: Professional Cloud Architect- This certification is ideal for professionals who want to design, develop, and manage Google Cloud solutions. This certification involves designing & planning cloud architecture, managing & provisioning cloud solutions, etc. It ensures security, compliance, analysis, and optimization of business processes. This certification is the best fit for IT professionals and cloud engineers working with Google Cloud. 

Certified Kubernetes Administrator- Kubernetes is a pivotal technology for container orchestration in cloud atmospheres. The CKA certification showcases your potential to deploy, govern, and troubleshoot Kubernetes groupings. It includes Kubernetes architecture, cluster installation, configuration, networking, security, troubleshooting, and managing workloads and scheduling. The devOps engineer and cloud-native application developers can pursue this program.

CompTIA Cloud+ - It is a vendor-neutral certification that offers a foundational understanding of cloud technologies. It is ideal for IT experts seeking a broad cloud computing knowledge base. This certification covers cloud deployment, cloud management, security, compliance, cloud infrastructure, and troubleshooting cloud environments. It is a great option for IT professionals, new to cloud computing, Python programming for DevOps, etc. 

AWS Certified: DevOps Engineer Professional - For those emphasizing Python programming for DevOps within AWS, this certification is highly regarded. It validates skills in continuous integration, monitoring, and deployment strategies. The key topics include continuous integration/ continuous deployment, infrastructure as a code, monitoring & logging, and security & compliance automation. The devOps engineers and cloud automation professionals. 

Microsoft Certified: Azure DevOps Engineer Expert- This certification is crafted for professionals who implement devOps strategies in Azure. It covers agile development, CI/CD, and infrastructure automation. The certification covers Azure pipelines & Repos, infrastructure as code using ARM templates, continuous monitoring & feedback, etc. in devOps. The devOps engineers using Azure and IT professionals focusing on automation can join this program.

Google Cloud Professional DevOps Engineer- This certification is suitable for individuals who specialize in deploying and maintaining applications on Google Cloud using DevOps methodologies. It covers site reliability engineering principles, CI/CD pipeline execution, service monitoring & incident response, and security. This credential is immaculate for cloud operation professionals and DevOps engineers. 

Certified Cloud Security Professionals- Cloud security is a potent crisis for businesses. The CCSP certification from (ISC)2 validates expertise in securing cloud environments. The certification covers cloud security architecture & design, risk management & compliance, identity and access management (IAM), and cloud application security. It is ideal for security analysts, and IT experts emphasizing cloud security. 

IBM Certified: Solutions Architect Cloud Pak For Data- It is gaining traction in artificial intelligence-driven cloud solutions. This certification is best fitted for professionals working with IBM cloud technologies. It covers data governance & security, AI & machine learning blend, cloud-native application maturation, and hybrid cloud strategies. This certification is best suited for data architects and AI/Cloud professionals. 

With rapid progress in cloud technologies, abiding competition mandates continuous wisdom and skill enhancement. Certifications offer a structured pathway to mastering cloud platforms, tools, and best practices. As businesses move towards digitalization, cloud computing remains an important element in the IT strategy. By obtaining an industry-recognized certification, you can future-proof your career and secure high-paying job opportunities. 

Conclusion

The cloud computing certification courses are capable of significantly impacting your career in 2025. If you want to specialize in cloud computing or only in devOps methodologies, the above-listed courses offer it all. These certifications are suited to your employment objectives. Join the 2025 cloud computing best certifications and carve your profile for higher compensation. The only way to select one is the one that suits your career goals better. 

[internsipgate]

Building Your Portfolio: DevOps Projects to Showcase During Your Internship

In the fast-evolving world of DevOps, a well-rounded portfolio can make all the difference when it comes to landing internships or securing full-time opportunities. Whether you’re new to DevOps or looking to enhance your skills, showcasing relevant projects in your portfolio demonstrates your technical abilities and problem-solving skills. Here’s how you can build a compelling DevOps portfolio with standout projects.https://internshipgate.com

Why a DevOps Portfolio Matters

A strong DevOps portfolio showcases your technical expertise and your ability to solve real-world challenges. It serves as a practical demonstration of your skills in:

Automation: Building pipelines and scripting workflows.

Collaboration: Managing version control and working with teams.

Problem Solving: Troubleshooting and optimizing system processes.

Tool Proficiency: Demonstrating your experience with tools like Docker, Kubernetes, Jenkins, Ansible, and Terraform.

By showcasing practical projects, you’ll not only impress potential recruiters but also stand out among other candidates with similar academic qualifications.

DevOps Projects to Include in Your Portfolio

Here are some project ideas you can work on to create a standout DevOps portfolio:

Automated CI/CD Pipeline

What it showcases: Your understanding of continuous integration and continuous deployment (CI/CD).

Description: Build a pipeline using tools like Jenkins, GitHub Actions, or GitLab CI/CD to automate the build, test, and deployment process. Use a sample application and deploy it to a cloud environment like AWS, Azure, or Google Cloud.

Key Features:

Code integration with GitHub.

Automated testing during the CI phase.

Deployment to a staging or production environment.

Containerized Application Deployment

What it showcases: Proficiency with containerization and orchestration tools.

Description: Containerize a web application using Docker and deploy it using Kubernetes. Demonstrate scaling, load balancing, and monitoring within your cluster.

Key Features:

Create Docker images for microservices.

Deploy the services using Kubernetes manifests.

Implement health checks and auto-scaling policies.

Infrastructure as Code (IaC) Project

What it showcases: Mastery of Infrastructure as Code tools like Terraform or AWS CloudFormation.

Description: Write Terraform scripts to create and manage infrastructure on a cloud platform. Automate tasks such as provisioning servers, setting up networks, and deploying applications.

Key Features:

Manage infrastructure through version-controlled code.

Demonstrate multi-environment deployments (e.g., dev, staging, production).

Monitoring and Logging Setup

What it showcases: Your ability to monitor applications and systems effectively.

Description: Set up a monitoring and logging system using tools like Prometheus, Grafana, or ELK Stack (Elasticsearch, Logstash, and Kibana). Focus on visualizing application performance and troubleshooting issues.

Key Features:

Dashboards displaying metrics like CPU usage, memory, and response times.

Alerts for critical failures or performance bottlenecks.

Cloud Automation with Serverless Frameworks

What it showcases: Familiarity with serverless architectures and cloud services.

Description: Create a serverless application using AWS Lambda, Azure Functions, or Google Cloud Functions. Automate backend tasks like image processing or real-time data processing.

Key Features:

Trigger functions through API Gateway or cloud storage.

Integrate with other cloud services such as DynamoDB or Firestore.

Version Control and Collaboration Workflow

What it showcases: Your ability to manage and collaborate on code effectively.

Description: Create a Git workflow for a small team, implementing branching strategies (e.g., Git Flow) and pull request reviews. Document the process with markdown files.

Key Features:

Multi-branch repository with clear workflows.

Documentation on resolving merge conflicts.

Clear guidelines for code reviews and commits.

Tips for Presenting Your Portfolio

Once you’ve completed your projects, it’s time to present them effectively. Here are some tips:

Use GitHub or GitLab

Host your project repositories on platforms like GitHub or GitLab. Use README files to provide an overview of each project, including setup instructions, tools used, and key features.

Create a Personal Website

Build a simple website to showcase your projects visually. Use tools like Hugo, Jekyll, or WordPress to create an online portfolio.

Write Blogs or Case Studies

Document your projects with detailed case studies or blogs. Explain the challenges you faced, how you solved them, and the outcomes.

Include Visuals and Demos

Add screenshots, GIFs, or video demonstrations to highlight key functionalities. If possible, include live demo links to deployed applications.

Organize by Skills

Arrange your portfolio by categories such as automation, cloud computing, or monitoring to make it easy for recruiters to identify your strengths.

Final Thoughtshttps://internshipgate.com

Building a DevOps portfolio takes time and effort, but the results are worth it. By completing and showcasing hands-on projects, you demonstrate your technical expertise and passion for the field. Start with small, manageable projects and gradually take on more complex challenges. With a compelling portfolio, you’ll be well-equipped to impress recruiters and excel in your internship interviews.

[hawkstack]

Kubernetes - Prometheus & Grafana

Introduction

Kubernetes is a powerful orchestration tool for containerized applications, but monitoring its health and performance is crucial for maintaining reliability. This is where Prometheus and Grafana come into play. Prometheus is a robust monitoring system that collects and stores time-series data, while Grafana provides rich visualization capabilities, making it easier to analyze metrics and spot issues.

In this post, we will explore how Prometheus and Grafana work together to monitor Kubernetes clusters, ensuring optimal performance and stability.

Why Use Prometheus and Grafana for Kubernetes Monitoring?

1. Prometheus - The Monitoring Powerhouse

Prometheus is widely used in Kubernetes environments due to its powerful features:

Time-series database: Efficiently stores metrics in a multi-dimensional format.

Kubernetes-native integration: Seamless discovery of pods, nodes, and services.

Powerful querying with PromQL: Enables complex queries to extract meaningful insights.

Alerting system: Supports rule-based alerts via Alertmanager.

2. Grafana - The Visualization Layer

Grafana transforms raw metrics from Prometheus into insightful dashboards:

Customizable dashboards: Tailor views to highlight key performance indicators.

Multi-source support: Can integrate data from multiple sources alongside Prometheus.

Alerting & notifications: Get notified about critical issues via various channels.

Setting Up Prometheus & Grafana in Kubernetes

1. Deploy Prometheus

Using Helm, you can install Prometheus in your Kubernetes cluster:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

helm repo update

helm install prometheus prometheus-community/kube-prometheus-stack

This will install Prometheus, Alertmanager, and related components.

2. Deploy Grafana

Grafana is included in the kube-prometheus-stack Helm chart, but if you want to install it separately:

helm install grafana grafana/grafana

After installation, retrieve the admin password and access Grafana:

kubectl get secret --namespace default grafana -o jsonpath="{.data.admin-password}" | base64 --decode

kubectl port-forward svc/grafana 3000:80

Access Grafana at http://localhost:3000 using the retrieved credentials.

3. Configure Prometheus as a Data Source

In Grafana:

Go to Configuration > Data Sources

Select Prometheus

Enter the Prometheus service URL (e.g., http://prometheus-server.default.svc.cluster.local:9090)

Click Save & Test

4. Import Kubernetes Dashboards

Grafana provides ready-made dashboards for Kubernetes. You can import dashboards by using community templates available on Grafana Dashboards.

Key Metrics to Monitor in Kubernetes

Some essential Kubernetes metrics to track using Prometheus and Grafana include:

Node Health: CPU, memory, disk usage

Pod & Container Performance: CPU and memory usage per pod

Kubernetes API Server Health: Request latency, error rates

Networking Metrics: Traffic in/out per pod, DNS resolution times

Custom Application Metrics: Business logic performance, request rates

Setting Up Alerts

Using Prometheus Alertmanager, you can configure alerts for critical conditions:

- alert: HighCPUUsage expr: avg(rate(container_cpu_usage_seconds_total[5m])) by (pod) > 0.8 for: 5m labels: severity: critical annotations: summary: "High CPU usage detected"Alerts can be sent via email, Slack, PagerDuty, and other integrations.

Conclusion Prometheus and Grafana provide a comprehensive monitoring and visualization solution for Kubernetes clusters. With the right setup, you can gain deep insights into your cluster’s performance, detect anomalies, and ensure high availability.

By integrating Prometheus' powerful data collection with Grafana’s intuitive dashboards, teams can efficiently manage and troubleshoot Kubernetes environments. Start monitoring today and take your Kubernetes operations to the next level!

For more details www.hawkstack.com