#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.

[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

[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. 

[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…

[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.

[tech-education-blog]

Kubernetes Full Course

Croma Campus offers a comprehensive Kubernetes Full Course, covering container orchestration, cluster management, deployments, scaling, and monitoring. Gain hands-on experience with Kubernetes architecture, pods, services, and troubleshooting techniques. Ideal for DevOps professionals and cloud enthusiasts looking to excel in modern infrastructure management. Enroll now for expert-led training!

[ludoonline]

Building a Reliable CI/CD Pipeline for Cloud-Native Applications

In the world of cloud-native application development, the need for rapid, reliable, and continuous delivery of software is paramount. This is where Continuous Integration (CI) and Continuous Deployment (CD) pipelines come into play. These automated pipelines help development teams streamline their processes, reduce manual errors, and accelerate the delivery of high-quality cloud applications.

Building a reliable CI/CD pipeline for cloud-native applications requires careful planning, the right tools, and best practices to ensure smooth operations. In this blog, we’ll explore the essential components of a successful CI/CD pipeline and the strategies to make it both reliable and efficient for cloud-native applications.

1. Understand the Core Concepts of CI/CD

Before diving into building the pipeline, it's crucial to understand the fundamental principles behind CI/CD:

Continuous Integration (CI): This practice involves automatically integrating new code into the main branch of the codebase several times a day. CI ensures that developers are constantly merging their changes into a shared repository, making the process of finding bugs easier and helping to keep the codebase up-to-date.

Continuous Deployment (CD): In this phase, code that has passed through various testing stages is automatically deployed to production. This means that once code is committed, it undergoes automated testing and, if successful, is deployed directly to the production environment without manual intervention.

For cloud-native applications, these practices ensure that the application’s deployment cycle is not only automated but also consistent, which is essential for scaling and maintaining cloud applications.

2. Selecting the Right Tools for CI/CD

To build a reliable CI/CD pipeline, you need the right set of tools to automate the integration, testing, and deployment processes. Popular CI/CD tools include:

Jenkins: One of the most popular open-source tools for automating builds and deployments. Jenkins can be configured to work with most cloud platforms and supports a wide array of plugins for CI/CD workflows.

GitLab CI/CD: GitLab provides an integrated DevOps platform that includes version control and CI/CD capabilities, enabling seamless integration of the entire software delivery lifecycle.

CircleCI: Known for its speed and scalability, CircleCI offers cloud-native CI/CD solutions that integrate well with Kubernetes and cloud-based environments.

GitHub Actions: An emerging tool for automating workflows within GitHub repositories, making it easier to set up CI/CD directly within the GitHub interface.

Travis CI: Another cloud-native tool that offers integration with various cloud environments, including AWS, Azure, and GCP.

Selecting the right CI/CD tool will depend on your team’s needs, the complexity of your application, and your cloud environment. It's essential to choose tools that integrate well with your cloud platform and support your preferred workflows.

3. Containerization and Kubernetes for Cloud-Native Apps

Cloud-native applications rely heavily on containers to ensure consistency across different environments (development, staging, production). This is where tools like Docker and Kubernetes come in.

Docker: Docker allows you to containerize your applications, ensuring that they run the same way on any environment. By creating a Dockerfile for your application, you can package it along with its dependencies, ensuring a consistent deployment across environments.

Kubernetes: Kubernetes is a container orchestration tool that helps manage containerized applications at scale. It automates deployments, scaling, and operations of application containers across clusters of hosts. Kubernetes is crucial for deploying cloud-native applications in the cloud, providing automated scaling, load balancing, and self-healing capabilities.

Integrating Docker and Kubernetes into your CI/CD pipeline ensures that your cloud-native application can be deployed seamlessly in a cloud environment, with the flexibility to scale as needed.

4. Automated Testing in CI/CD Pipelines

Automated testing is a critical component of a reliable CI/CD pipeline. Testing ensures that code changes do not introduce bugs or break functionality. In cloud-native applications, automated testing should be incorporated into every stage of the CI/CD pipeline:

Unit Tests: Test individual components or functions of your application to ensure that the core logic is working as expected.

Integration Tests: Ensure that different parts of the application interact correctly with each other. These tests are crucial for cloud-native applications, where services often communicate across multiple containers or microservices.

End-to-End Tests: Test the application as a whole, simulating user interactions to ensure that the entire application behaves as expected in a production-like environment.

Performance Tests: Test the scalability and performance of your application under different loads. This is especially important for cloud-native applications, which must handle varying workloads and traffic spikes.

Automating these tests within the pipeline ensures that issues are identified early, reducing the time and cost of fixing them later in the process.

5. Continuous Monitoring and Feedback Loops

A reliable CI/CD pipeline doesn’t stop at deployment. Continuous monitoring and feedback are essential for maintaining the health of your cloud-native application.

Monitoring Tools: Use tools like Prometheus, Grafana, or Datadog to continuously monitor your application’s performance in the cloud. These tools provide real-time insights into application behavior, helping you identify bottlenecks and issues before they impact users.

Feedback Loops: Set up automated feedback loops that alert your team to failures, errors, or performance issues. With cloud-native applications, where services and components are distributed, real-time feedback is essential for maintaining high availability and performance.

Incorporating continuous monitoring into your CI/CD pipeline ensures that your application stays healthy and optimized after deployment, enabling rapid iteration and continuous improvement.

6. Version Control Integration

Version control is at the heart of CI/CD. For cloud-native applications, Git is the most popular version control system used for managing code changes.

Branching Strategies: Implement a branching strategy that works for your team and application. Popular strategies like GitFlow and Feature Branching help ensure smooth collaboration among development teams and facilitate automated deployments through the pipeline.

Commit and Pull Request Workflow: Ensure that every commit is reviewed and tested automatically through the CI/CD pipeline. Pull requests trigger the CI/CD process, which runs tests and, if successful, merges the changes into the main branch for deployment.

Version control integration ensures that your code is always up-to-date, maintains a clear history of changes, and triggers automated processes when changes are committed.

7. Security in the CI/CD Pipeline

Security must be a top priority when building your CI/CD pipeline, especially for cloud-native applications. Integrating security practices into the CI/CD pipeline ensures that vulnerabilities are detected early, and sensitive data is protected.

Static Code Analysis: Integrate tools like SonarQube or Snyk to perform static code analysis during the CI phase. These tools scan your codebase for known vulnerabilities and coding issues.

Secret Management: Use tools like HashiCorp Vault or AWS Secrets Manager to securely manage sensitive information such as API keys, database passwords, and other credentials. Avoid hardcoding sensitive data in your source code.

Container Security: Perform security scans on your Docker images using tools like Clair or Aqua Security to identify vulnerabilities in containerized applications before deployment.

Building security into your CI/CD pipeline (often referred to as DevSecOps) ensures that your cloud-native applications are secure by design and compliant with industry regulations.

8. Best Practices for a Reliable CI/CD Pipeline

To build a truly reliable CI/CD pipeline, here are some best practices:

Keep Pipelines Simple and Modular: Break your CI/CD pipeline into smaller, manageable stages that are easier to maintain and troubleshoot.

Automate as Much as Possible: From testing to deployment, automation is the key to a reliable pipeline.

Monitor Pipeline Health: Regularly monitor the health of your pipeline and address failures quickly to avoid delays in the deployment process.

Rollback Mechanisms: Ensure that your pipeline includes automated rollback mechanisms for quick recovery if something goes wrong during deployment.

By following these best practices, you can ensure that your CI/CD pipeline is efficient, reliable, and capable of handling the complexities of cloud-native applications.

Conclusion

Building a reliable CI/CD pipeline for cloud-native applications is essential for enabling fast, frequent, and high-quality deployments. By integrating automation, containerization, security, and continuous monitoring into your pipeline, you can ensure that your cloud-native applications are delivered quickly and reliably, while minimizing risks.

By choosing the right tools, implementing automated testing, and following best practices, organizations can enhance the efficiency of their software development lifecycle, enabling teams to innovate faster and deliver value to their customers.

For organizations looking to optimize their cloud-native CI/CD pipelines, Salzen offers expertise and solutions to help streamline the process, ensuring faster delivery and high-quality results for every deployment.

[hawkstack]

OpenShift vs Kubernetes: Key Differences Explained

Kubernetes has become the de facto standard for container orchestration, enabling organizations to manage and scale containerized applications efficiently. However, OpenShift, built on top of Kubernetes, offers additional features that streamline development and deployment. While they share core functionalities, they have distinct differences that impact their usability. In this blog, we explore the key differences between OpenShift and Kubernetes.

1. Core Overview

Kubernetes:

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and operation of application containers. It provides the building blocks for containerized workloads but requires additional tools for complete enterprise-level functionality.

OpenShift:

OpenShift is a Kubernetes-based container platform developed by Red Hat. It provides additional features such as a built-in CI/CD pipeline, enhanced security, and developer-friendly tools to simplify Kubernetes management.

2. Installation & Setup

Kubernetes:

Requires manual installation and configuration.

Cluster setup involves configuring multiple components such as kube-apiserver, kube-controller-manager, kube-scheduler, and networking.

Offers flexibility but requires expertise to manage.

OpenShift:

Provides an easier installation process with automated scripts.

Includes a fully integrated web console for management.

Requires Red Hat OpenShift subscriptions for enterprise-grade support.

3. Security & Authentication

Kubernetes:

Security policies and authentication need to be manually configured.

Role-Based Access Control (RBAC) is available but requires additional setup.

OpenShift:

Comes with built-in security features.

Uses Security Context Constraints (SCCs) for enhanced security.

Integrated authentication mechanisms, including OAuth and LDAP support.

4. Networking

Kubernetes:

Uses third-party plugins (e.g., Calico, Flannel, Cilium) for networking.

Network policies must be configured separately.

OpenShift:

Uses Open vSwitch-based SDN by default.

Provides automatic service discovery and routing.

Built-in router and HAProxy-based load balancing.

5. Development & CI/CD Integration

Kubernetes:

Requires third-party tools for CI/CD (e.g., Jenkins, ArgoCD, Tekton).

Developers must integrate CI/CD pipelines manually.

OpenShift:

Comes with built-in CI/CD capabilities via OpenShift Pipelines.

Source-to-Image (S2I) feature allows developers to build images directly from source code.

Supports GitOps methodologies out of the box.

6. User Interface & Management

Kubernetes:

Managed through the command line (kubectl) or third-party UI tools (e.g., Lens, Rancher).

No built-in dashboard; requires separate installation.

OpenShift:

Includes a built-in web console for easier management.

Provides graphical interfaces for monitoring applications, logs, and metrics.

7. Enterprise Support & Cost

Kubernetes:

Open-source and free to use.

Requires skilled teams to manage and maintain infrastructure.

Support is available from third-party providers.

OpenShift:

Requires a Red Hat subscription for enterprise support.

Offers enterprise-grade stability, support, and compliance features.

Managed OpenShift offerings are available via cloud providers (AWS, Azure, GCP).

Conclusion

Both OpenShift and Kubernetes serve as powerful container orchestration platforms. Kubernetes is highly flexible and widely adopted, but it demands expertise for setup and management. OpenShift, on the other hand, simplifies the experience with built-in security, networking, and developer tools, making it a strong choice for enterprises looking for a robust, supported Kubernetes distribution.

Choosing between them depends on your organization's needs: if you seek flexibility and open-source freedom, Kubernetes is ideal; if you prefer an enterprise-ready solution with out-of-the-box tools, OpenShift is the way to go.

For more details click www.hawkstack.com 

[exit-certified]

Top 7 IT Certifications to Pursue in 2025

Are you wondering how to stay competitive in the fast-paced world of IT? With technology evolving at lightning speed, staying ahead of the curve means more than just keeping up—it means proving your skills and expertise. That’s where IT certificationscome in. Whether you’re just starting out or looking to specialize in a high-demand area like cloud computing or cybersecurity, the right certification can open doors to exciting opportunities and help you stand out in a crowded job market.

As we dive into 2025, some certifications are emerging as essential for IT professionals aiming to boost their careers. From cloud architects to security experts, these credentials showcase your ability to handle the latest technologies and challenges. Let’s explore the top seven IT certifications that can take your career to the next level this year.

1. AWS Certified Solutions Architect – Associate

Cloud computing continues to dominate IT landscapes, and Amazon Web Services (AWS) remains a market leader. The AWS Certified Solutions Architect – Associate certification is ideal for professionals looking to design scalable and secure cloud solutions. This certification demonstrates your ability to deploy systems on AWS, optimize performance, and ensure cost efficiency. With cloud adoption growing across industries, this credential is in high demand.

2. Certified Information Systems Security Professional (CISSP)

Cybersecurity threats are more sophisticated than ever, making the CISSP certification a critical asset. Recognized globally, CISSP validates expertise in designing and managing an organization’s security program. It covers eight domains, including risk management, asset security, and software development security. If you’re aiming for senior roles in cybersecurity, such as Security Manager or Chief Information Security Officer (CISO), CISSP is the gold standard.

3. Microsoft Certified: Azure Solutions Architect Expert

Microsoft Azure is a strong competitor to AWS, with increasing adoption among enterprises. The Azure Solutions Architect Expert certification equips professionals with skills to design cloud and hybrid solutions. This IT certification is perfect for those looking to leverage Azure’s growing ecosystem, particularly in environments that rely on Microsoft’s products and services.

4. CompTIA Security+

For those beginning their cybersecurity journey, CompTIA Security+ is a foundational certification that’s recognized worldwide. It covers essential security concepts, including network security, threats, vulnerabilities, and risk management. As an entry-level certification, it’s an excellent stepping stone for IT professionals looking to specialize in cybersecurity without prior experience.

5. Google Cloud Professional Cloud Architect

With Google Cloud gaining traction, this certification is increasingly valuable. It validates your ability to design, develop, and manage secure, robust cloud solutions on Google Cloud Platform (GCP). As businesses diversify their cloud strategies, having a Google Cloud IT certification positions you as a versatile professional capable of supporting multi-cloud environments.

6. Certified Kubernetes Administrator (CKA)

Containerization and orchestration are essential skills in modern IT, and Kubernetes is at the forefront of this trend. The Certified Kubernetes Administrator (CKA) certification is ideal for professionals managing containerized applications and clusters. With the rise of DevOps and microservices architectures, this certification ensures your expertise remains relevant.

7. PMI Project Management Professional (PMP)

While not exclusive to IT, the PMP certification is highly sought after in technology-driven projects. It demonstrates proficiency in managing complex projects, ensuring deadlines and budgets are met. For IT professionals looking to transition into managerial roles, PMP is a game-changer.

Why IT Certifications Matter in 2025

The tech industry evolves rapidly, and staying updated with relevant IT certifications is key to staying competitive. Certifications not only validate your technical skills but also show your commitment to professional growth. 

Plus, employers value certified professionals for their ability to handle the latest technologies and methodologies effectively. So, choosing the right IT certification in 2025 can significantly enhance your career prospects.   For more information visit: https://www.exitcertified.com/certification

[ericvanderburg]

Multi-Cluster Kubernetes Sealed Secrets Management in Jenkins

http://securitytc.com/THV260