#Kubernetes in Cloud Instances

[virtualizationhowto]

k0s vs k3s - Battle of the Tiny Kubernetes distros

k0s vs k3s - Battle of the Tiny Kubernetes distros #100daysofhomelab #homelab @vexpert #vmwarecommunities #KubernetesDistributions, #k0svsk3s, #RunningKubernetes, #LightweightKubernetes, #KubernetesInEdgeComputing, #KubernetesInBareMetal

Kubernetes has redefined the management of containerized applications. The rich ecosystem of Kubernetes distributions testifies to its widespread adoption and versatility. Today, we compare k0s vs k3s, two unique Kubernetes distributions designed to seamlessly run Kubernetes across varied infrastructures, from cloud instances to bare metal and edge computing settings. Those with home labs will…

[ajpandey1]

AEM aaCS aka Adobe Experience Manager as a Cloud Service

As the industry standard for digital experience management, Adobe Experience Manager is now being improved upon. Finally, Adobe is transferring Adobe Experience Manager (AEM), its final on-premises product, to the cloud.

AEM aaCS is a modern, cloud-native application that accelerates the delivery of omnichannel application.

The AEM Cloud Service introduces the next generation of the AEM product line, moving away from versioned releases like AEM 6.4, AEM 6.5, etc. to a continuous release with less versioning called "AEM as a Cloud Service."

AEM Cloud Service adopts all benefits of modern cloud based services:

Availability

The ability for all services to be always on, ensuring that our clients do not suffer any downtime, is one of the major advantages of switching to AEM Cloud Service. In the past, there was a requirement to regularly halt the service for various maintenance operations, including updates, patches, upgrades, and certain standard maintenance activities, notably on the author side.

Scalability

The AEM Cloud Service's instances are all generated with the same default size. AEM Cloud Service is built on an orchestration engine (Kubernetes) that dynamically scales up and down in accordance with the demands of our clients without requiring their involvement. both horizontally and vertically. Based on, scaling can be done manually or automatically.

Updated Code Base

This might be the most beneficial and much anticipated function that AEM Cloud Service offers to consumers. With the AEM Cloud Service, Adobe will handle upgrading all instances to the most recent code base. No downtime will be experienced throughout the update process.

Self Evolving

Continually improving and learning from the projects our clients deploy, AEM Cloud Service. We regularly examine and validate content, code, and settings against best practices to help our clients understand how to accomplish their business objectives. AEM cloud solution components that include health checks enable them to self-heal.

AEM as a Cloud Service: Changes and Challenges

When you begin your work, you will notice a lot of changes in the aem cloud jar. Here are a few significant changes that might have an effect on how we now operate with aem:-

1)The significant exhibition bottleneck that the greater part of huge endeavor DAM clients are confronting is mass transferring of resource on creator example and afterward DAM Update work process debase execution of entire creator occurrence. To determine this AEM Cloud administration brings Resource Microservices for serverless resource handling controlled by Adobe I/O. Presently when creator transfers any resource it will go straightforwardly to cloud paired capacity then adobe I/O is set off which will deal with additional handling by utilizing versions and different properties that has been designed.

2)Due to Adobe's complete management of AEM cloud service, developers and operations personnel may not be able to directly access logs. As of right now, the only way I know of to request access, error, dispatcher, and other logs will be via a cloud manager download link.

3)The only way for AEM Leads to deploy is through cloud manager, which is subject to stringent CI/CD pipeline quality checks. At this point, you should concentrate on test-driven development with greater than 50% test coverage. Go to https://docs.adobe.com/content/help/en/experience-manager-cloud-manager/using/how-to-use/understand-your-test-results.html for additional information.

4)AEM as a cloud service does not currently support AEM screens or AEM Adaptive forms.

5)Continuous updates will be pushed to the cloud-based AEM Base line image to support version-less solutions. Consequently, any Asset UI console or libs granite customizations: Up until AEM 6.5, the internal node, which could be used as a workaround to meet customer requirements, is no longer possible because it will be replaced with each base line image update.

6)Local sonar cannot use the code quality rules that are available in cloud manager before pushing to git. which I believe will result in increased development time and git commits. Once the development code is pushed to the git repository and the build is started, cloud manager will run sonar checks and tell you what's wrong. As a precaution, I recommend that you do not have any problems with the default rules in your local environment and that you continue to update the rules whenever you encounter them while pushing the code to cloud git.

AEM Cloud Service Does Not Support These Features

1.AEM Sites Commerce add-on 2.Screens add-on 3.Networks add-on 4.AEM Structures 5.Admittance to Exemplary UI. 6.Page Editor is in Developer Mode. 7./apps or /libs are ready-only in dev/stage/prod environment – changes need to come in via CI/CD pipeline that builds the code from the GIT repo. 8.OSGI bundles and settings: the dev, stage, and production environments do not support the web console.

If you encounter any difficulties or observe any issue , please let me know. It will be useful for AEM people group.

[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.

[hiringjournal]

The Cost of Hiring a Microservices Engineer: What to Expect

Many tech businesses are switching from monolithic programs to microservices-based architectures as software systems get more complicated. More flexibility, scalability, and deployment speed are brought about by this change, but it also calls for specialized talent. Knowing how much hiring a microservices engineer would cost is essential to making an informed decision.

Understanding the factors that affect costs can help you better plan your budget and draw in the best personnel, whether you're developing a new product or updating outdated systems.

Budgeting for Specialized Talent in a Modern Cloud Architecture

Applications composed of tiny, loosely linked services are designed, developed, and maintained by microservices engineers. These services are frequently implemented separately and communicate via APIs. When you hire a microservices engineer they should have extensive experience with distributed systems, API design, service orchestration, and containerization.

They frequently work with cloud platforms like AWS, Azure, or GCP as well as tools like Docker, Kubernetes, and Spring Boot. They play a crucial part in maintaining the scalability, modularity, and maintainability of your application.

What Influences the Cost?

The following variables affect the cost of hiring a microservices engineer:

1. Level of Experience

Although they might charge less, junior engineers will probably require supervision. Because they can independently design and implement reliable solutions, mid-level and senior engineers with practical experience in large-scale microservices projects attract higher rates.

2. Place

Geography has a major impact on salaries. Hiring in North America or Western Europe, for instance, is usually more expensive than hiring in Southeast Asia, Eastern Europe, or Latin America.

3. Type of Employment

Are you hiring contract, freelance, or full-time employees? For short-term work, freelancers may charge higher hourly rates, but the total project cost may be less.

4. Specialization and the Tech Stack

Because of their specialised knowledge, engineers who are familiar with niche stacks or tools (such as event-driven architecture, Istio, or advanced Kubernetes usage) frequently charge extra.

Use a salary benchmarking tool to ensure that your pay is competitive. This helps you set expectations and prevent overpaying or underbidding by providing you with up-to-date market data based on role, region, and experience.

Hidden Costs to Consider

In addition to the base pay or rate, you need account for:

Time spent onboarding and training

Time devoted to applicant evaluation and interviews

The price of bad hires (in terms of rework or delays)

Continuous assistance and upkeep if you're starting from scratch

These elements highlight how crucial it is to make a thoughtful, knowledgeable hiring choice.

Complementary Roles to Consider

Working alone is not how a microservices engineer operates. Several tech organizations also hire cloud engineers to oversee deployment pipelines, networking, and infrastructure. Improved production performance and easier scaling are guaranteed when these positions work closely together.

Summing Up

Hiring a microservices engineer is a strategic investment rather than merely a cost. These engineers with the appropriate training and resources lays the groundwork for long-term agility and scalability.

Make smart financial decisions by using tools such as a pay benchmarking tool, and think about combining your hire with cloud or DevOps support. The correct engineer can improve your architecture's speed, stability, and long-term value for tech businesses updating their apps.

[ascendient-learning]

Invigorate Your IT Potential with VMware Training from Ascendient Learning 

VMware is at the forefront of virtualization solutions, powering software-defined data centers, hybrid clouds, and secure infrastructure management for enterprises worldwide. With over 500,000 customers globally, including all Fortune 500 companies, VMware expertise significantly enhances your value as an IT professional. 

Ascendient Learning, named VMware's North American Learning Partner of the Year in 2023, offers comprehensive, industry-leading VMware training to help you stay competitive. 

Comprehensive VMware Training at Ascendient Learning 

Ascendient Learning offers an extensive portfolio of VMware-certified courses covering the most critical VMware technologies. Training is available for: 

vSphere: The foundational technology for software-defined data centers. Courses like "VMware vSphere: Install, Configure, Manage [V8]" and "Operate, Scale and Secure [V8]" provide critical virtualization and management skills. 

NSX: VMware NSX courses teach vital network virtualization and cybersecurity skills. Popular courses include "VMware NSX: Install, Configure, Manage [V4.0]" and "NSX: Troubleshooting and Operations." 

vSAN: This training equips professionals to efficiently deploy and manage software-defined storage solutions. Courses like "VMware vSAN: Install, Configure, Manage [V8]" and "VMware vSAN: Troubleshooting [V8]" ensure you’re skilled in the latest storage innovations. 

vRealize Suite: Ascendient offers training on advanced cloud automation and orchestration tools, crucial for streamlining IT processes and infrastructure management. 

Tanzu and Kubernetes: Ascendient’s Tanzu courses, including "VMware vSphere with Tanzu: Deploy, Configure, Manage," empower IT teams to build and manage modern cloud-native applications efficiently. 

VMware Aria Suite: Training in VMware Aria helps professionals achieve advanced operational insights and efficient cloud automation management, including "VMware Aria Automation: Orchestration and Extensibility." 

Flexible Training Formats Designed for Real-Life Schedules 

Ascendient Learning recognizes the need for training that adapts to your busy professional life. Therefore, VMware training is offered in various convenient formats: 

Instructor-Led Virtual Sessions: Participate interactively with expert instructors in real-time virtual environments. 

Guaranteed-to-Run Classes: Ascendient provides one of North America's largest Guaranteed-to-Run (GTR) VMware course schedules, offering reliability and predictable scheduling. 

Self-Paced Online Learning: Ideal for professionals seeking complete flexibility, these courses allow learners to progress at their own pace without compromising content quality or depth. 

In-Person Classroom Training: Engage directly with instructors and peers through traditional classroom-based training, fostering collaboration and hands-on practice. 

Real Benefits: Proven ROI for Professionals and Organizations  

Investing in VMware training with Ascendient Learning delivers tangible benefits. According to recent research, organizations with VMware-trained teams experience increased productivity, improved employee satisfaction, and reduced employee turnover. Ascendient learners have successfully leveraged VMware skills to secure promotions, negotiate salary increases, and transition into high-demand roles like Solutions Architects, Systems Engineers, Cloud Architects, and Network Specialists.  

For instance, companies implementing VMware vSAN and NSX through certified professionals have reported drastic improvements in data center efficiency, significantly lowering costs while boosting infrastructure performance and security. 

Your Path to VMware Certification Starts Here 

VMware-certified professionals are consistently in high demand. Achieving VMware certification through Ascendient Learning positions you strategically within the IT landscape, opening doors to better opportunities, higher salaries, and greater professional satisfaction. The industry increasingly values and rewards VMware expertise, making this training a strategic investment for both individual career growth and organizational success.   

Take the next step today. Join the thousands who have accelerated their careers and transformed their organizations through VMware training at Ascendient Learning. 

Enroll with Ascendient Learning and master VMware technology to shape your future in IT leadership. 

For more information visit: https://www.ascendientlearning.com/it-training/vmware

[govindhtech]

FinOps Hub 2.0 Removes Cloud Waste With Smart Analytics

FinOps Hub 2.0

As Google Cloud customers used FinOps Hub 2.0 to optimise, business feedback increased. Businesses often lack clear insights into resource consumption, creating a blind spot. DevOps users have tools and utilisation indicators to identify waste.

The latest State of FinOps 2025 Report emphasises waste reduction and workload optimisation as FinOps priorities. If customers don't understand consumption, workloads and apps are hard to optimise. Why get a committed usage discount for computing cores you may not be using fully?

Using paid resources more efficiently is generally the easiest change customers can make. The improved FinOps Hub in 2025 focusses on cleaning up optimisation possibilities to help you find, highlight, and eliminate unnecessary spending.

Discover waste: FinOps Hub 2.0 now includes utilisation data to identify optimisation opportunities.

FinOps Hub 2.0 released at Google Cloud Next 2025 to highlight resource utilisation statistics to discover waste and take immediate action. Waste can be an overprovisioned virtual machine (VM) that is barely used at 5%, an underprovisioned GKE cluster that is running hot at 110% utilisation and may fail, or managed resources like Cloud Run instances that are configured suboptimally or never used.

FinOps users may now display the most expensive waste category in a single heatmap per service or AppHub application. FinOps Hub not only identifies waste but also delivers cost savings for Cloud Run, Compute Engine, Kubernetes Engine, and Cloud SQL.

Highlight waste: FinOps Hub uses Gemini Cloud Assist for optimisation and engineering.

The fact that it utilises Gemini Cloud Assist to speed up FinOps Hub's most time-consuming tasks may make this version a 2.0. Since January 2024 to January 2025, Gemini Cloud Assist has saved clients over 100,000 FinOps hours a year by providing customised cost reports and synthesising insights.

Google Cloud offered FinOps Hub two options to simplify and automate procedures using Gemini Cloud Assist. FinOps can now get embedded optimisation insights on the hub, such cost reports, so you don't have to find the optimisation "needle in the haystack". Second, Gemini Cloud Assist can now assemble and provide the most significant waste insights to your engineering teams for speedy fixes.

Eliminate waste: Give IT solution owners a NEW IAM role authorisation to view and act on optimisation opportunities.

Tech solution owners now have access to the billing panel, FinOps' most anticipated feature. This will display Gemini Cloud Assist and FinOps data for all projects in one window. With multi-project views in the billing console, you can give a department that only uses a subset of projects for their infrastructure access to FinOps Hub or cost reports without giving them more billing data while still letting them view all of their data in one view.

The new Project Billing Costs Manager IAM role (or granular permissions) provides multi-project views. Sign up for the private preview of these new permissions. With increased access limitations, you may fully utilise FinOps solutions across your firm.

“With clouds overgrown, like winter’s old grime, spring clean your servers, save dollars and time.” Clean your cloud infrastructure with FinOps Hub 2.0 and Gemini Cloud Assist this spring. Whatever, Gemini says so.

[technocourses]

Getting Started with Google Kubernetes Engine: Your Gateway to Cloud-Native Greatness

After spending over 8 years deep in the trenches of cloud engineering and DevOps, I can tell you one thing for sure: if you're serious about scalability, flexibility, and real cloud-native application deployment, Google Kubernetes Engine (GKE) is where the magic happens.

Whether you’re new to Kubernetes or just exploring managed container platforms, getting started with Google Kubernetes Engine is one of the smartest moves you can make in your cloud journey.

"Containers are cool. Orchestrated containers? Game-changing."

🚀 What is Google Kubernetes Engine (GKE)?

Google Kubernetes Engine is a fully managed Kubernetes platform that runs on top of Google Cloud. GKE simplifies deploying, managing, and scaling containerized apps using Kubernetes—without the overhead of maintaining the control plane.

Why is this a big deal?

Because Kubernetes is notoriously powerful and notoriously complex. With GKE, Google handles all the heavy lifting—from cluster provisioning to upgrades, logging, and security.

"GKE takes the complexity out of Kubernetes so you can focus on building, not babysitting clusters."

🧭 Why Start with GKE?

If you're a developer, DevOps engineer, or cloud architect looking to:

Deploy scalable apps across hybrid/multi-cloud

Automate CI/CD workflows

Optimize infrastructure with autoscaling & spot instances

Run stateless or stateful microservices seamlessly

Then GKE is your launchpad.

Here’s what makes GKE shine:

Auto-upgrades & auto-repair for your clusters

Built-in security with Shielded GKE Nodes and Binary Authorization

Deep integration with Google Cloud IAM, VPC, and Logging

Autopilot mode for hands-off resource management

Native support for Anthos, Istio, and service meshes

"With GKE, it's not about managing containers—it's about unlocking agility at scale."

🔧 Getting Started with Google Kubernetes Engine

Ready to dive in? Here's a simple flow to kick things off:

Set up your Google Cloud project

Enable Kubernetes Engine API

Install gcloud CLI and Kubernetes command-line tool (kubectl)

Create a GKE cluster via console or command line

Deploy your app using Kubernetes manifests or Helm

Monitor, scale, and manage using GKE dashboard, Cloud Monitoring, and Cloud Logging

If you're using GKE Autopilot, Google manages your node infrastructure automatically—so you only manage your apps.

“Don’t let infrastructure slow your growth. Let GKE scale as you scale.”

🔗 Must-Read Resources to Kickstart GKE

👉 GKE Quickstart Guide – Google Cloud

👉 Best Practices for GKE – Google Cloud

👉 Anthos and GKE Integration

👉 GKE Autopilot vs Standard Clusters

👉 Google Cloud Kubernetes Learning Path – NetCom Learning

🧠 Real-World GKE Success Stories

A FinTech startup used GKE Autopilot to run microservices with zero infrastructure overhead

A global media company scaled video streaming workloads across continents in hours

A university deployed its LMS using GKE and reduced downtime by 80% during peak exam seasons

"You don’t need a huge ops team to build a global app. You just need GKE."

🎯 Final Thoughts

Getting started with Google Kubernetes Engine is like unlocking a fast track to modern app delivery. Whether you're running 10 containers or 10,000, GKE gives you the tools, automation, and scale to do it right.

With Google Cloud’s ecosystem—from Cloud Build to Artifact Registry to operations suite—GKE is more than just Kubernetes. It’s your platform for innovation.

“Containers are the future. GKE is the now.”

So fire up your first cluster. Launch your app. And let GKE do the heavy lifting while you focus on what really matters—shipping great software.

Let me know if you’d like this formatted into a visual infographic or checklist to go along with the blog!

[digital-working]

GCP Cloud Consulting Services to Elevate Your Cloud Strategy

Visit us Now - https://goognu.com/services/gcp-consulting-services 

Maximize your cloud investment with our GCP Cloud Consulting Services designed to accelerate innovation, enhance security, and reduce operational complexity. We help businesses harness the power of Google Cloud Platform (GCP) with tailored solutions for every stage of their cloud journey.

Our services include cloud readiness assessments, GCP migration, infrastructure design, DevOps implementation, and cloud-native development. Whether you're planning a cloud adoption strategy or optimizing an existing environment, our certified GCP consultants provide strategic guidance and hands-on expertise.

We help organizations modernize legacy systems by leveraging GCP’s cutting-edge offerings such as BigQuery, Cloud Functions, Kubernetes (GKE), and App Engine. Our consultants architect scalable, high-performance solutions to support real-time analytics, serverless applications, and advanced workloads.

Security and compliance are built into every solution. We deliver IAM setup, data encryption, VPC configuration, and compliance auditing to meet industry regulations and protect sensitive information. Our proactive security model ensures your cloud remains resilient against threats.

Through cost analysis and cloud optimization, we help reduce waste and improve efficiency by identifying underutilized resources, recommending reserved instances, and right-sizing deployments. We aim to maximize value without sacrificing performance.

[kuberneteskubevault]

Learn HashiCorp Vault in Kubernetes Using KubeVault

In today's cloud-native world, securing secrets, credentials, and sensitive configurations is more important than ever. That’s where Vault in Kubernetes becomes a game-changer — especially when combined with KubeVault, a powerful operator for managing HashiCorp Vault within Kubernetes clusters.

🔐 What is Vault in Kubernetes?

Vault in Kubernetes refers to the integration of HashiCorp Vault with Kubernetes to manage secrets dynamically, securely, and at scale. Vault provides features like secrets storage, access control, dynamic secrets, and secrets rotation — essential tools for modern DevOps and cloud security.

🚀 Why Use KubeVault?

KubeVault is an open-source Kubernetes operator developed to simplify Vault deployment and management inside Kubernetes environments. Whether you’re new to Vault or running production workloads, KubeVault automates:

Deployment and lifecycle management of Vault

Auto-unsealing using cloud KMS providers

Seamless integration with Kubernetes RBAC and CRDs

Secure injection of secrets into workloads

🛠️ Getting Started with KubeVault

Here's a high-level guide on how to deploy Vault in Kubernetes using KubeVault:

Install the KubeVault Operator Use Helm or YAML manifests to install the operator in your cluster. helm repo add appscode https://charts.appscode.com/stable/

helm install kubevault-operator appscode/kubevault --namespace kubevault --create-namespace

Deploy a Vault Server Define a custom resource (VaultServer) to spin up a Vault instance.

Configure Storage and Unsealer Use backends like GCS, S3, or Azure Blob for Vault storage and unseal via cloud KMS.

Inject Secrets into Workloads Automatically mount secrets into pods using Kubernetes-native integrations.

💡 Benefits of Using Vault in Kubernetes with KubeVault

✅ Automated Vault lifecycle management

✅ Native Kubernetes authentication

✅ Secret rotation without downtime

✅ Easy policy management via CRDs

✅ Enterprise-level security with minimal overhead

🔄 Real Use Case: Dynamic Secrets for Databases

Imagine your app requires database credentials. Instead of hardcoding secrets or storing them in plain YAML files, you can use KubeVault to dynamically generate and inject secrets directly into pods — with rotation and revocation handled automatically.

🌐 Final Thoughts

If you're deploying applications in Kubernetes, integrating Vault in Kubernetes using KubeVault isn't just a best practice — it's a security necessity. KubeVault makes it easy to run Vault at scale, without the hassle of manual configuration and operations.

Want to learn more? Check out KubeVault.com — the ultimate toolkit for managing secrets in Kubernetes using HashiCorp Vault.

[industrystudyreport]

Hybrid and Multi-Cloud Strategies: Shaping the APAC Cloud Market

Asia Pacific Cloud Computing Market Growth & Trends

The Asia Pacific Cloud Computing Market size is expected to reach USD 364.00 billion by 2030, growing at a CAGR of 16.6%, according to a new study conducted by Grand View Research, Inc. The numerous factors contributing to the growth of cloud computing in the Asia Pacific region include the expansion of digital transformation among organizations, increasing internet and mobile device penetration across the region, and increasing Big Data consumption.

An increasing number of cloud providers in the Asia Pacific region are actively developing cloud strategies to address business continuity and compliance requirements. For instance, in April 2023, Oracle Corporation announced to open a second cloud region in Singapore. The company’s new region will offer various services and applications including Oracle Container Engine for Kubernetes, MySQL HeatWave Database Service, Oracle Cloud VMware Solution, and Oracle Autonomous Database for small & medium businesses across manufacturing, financial services, retail, healthcare, and telecommunications in Southeast Asia.

End-use industries in the region are upgrading their data centers to offer better cloud solutions that can be combined with analytics technologies to suit business objectives and enhance business performance. Market players are also focused on expanding cloud services in the Asia Pacific region, which is anticipated to drive market growth. For instance, in June 2021, Alibaba Cloud announced the expansion of its services in Asia by introducing its first data center in the Philippines. The new data center has assisted the company in expanding its service offerings and gaining a competitive edge in the market.

Government bodies across the APAC region are undertaking initiatives to increase the adoption of cloud computing technologies across their countries. For instance, in August 2022, the National e-Governance Division (NeGD) of the Ministry of Electronics and Information Technology (MeitY), India, organized a Cloud Computing Capacity Building program for officials from State/UT Departments, Central Line Ministries, e-Government Project Directors, Mission Mode Projects, and State E-Mission Teams. This program is designed to ensure and impart adequate knowledge, appropriate skill, and appropriate competencies for utilizing the benefits of cloud computing in e-Governance practices. Moreover, hybrid cloud computing enables companies to free up local resources for more sensitive data or applications without spending on handling temporary surges in demand.

Curious about the Asia Pacific Cloud Computing Market? Download your FREE sample copy now and get a sneak peek into the latest insights and trends.

Asia Pacific Cloud Computing Market Report Highlights

The Infrastructure as a Service (IaaS) segment is expected to register the highest CAGR from 2023 to 2030, owing to the rising demand for low-cost IT infrastructure and faster data accessibility

The small & medium enterprises segment is expected to grow at the highest CAGR over the forecast period, owing to enhanced collaboration, easy accessibility, and quick turnaround times

Hybrid deployment is anticipated to be the fastest-growing segment over the forecast period. Hybrid cloud computing enables organizations to scale up their on-premise infrastructure to the public cloud to manage overflow when the computing and processing demand fluctuates

The manufacturing end-use segment is expected to register the highest growth rate from 2023 to 2030To improve operational resilience and efficiently manage upcoming risks and supply chain crises, manufacturers are leveraging cloud computing that is anticipated to drive the segment growth

Asia Pacific Cloud Computing Market Segmentation

Grand View Research has segmented the Asia Pacific cloud computing market based on service, deployment, enterprise size, end-use, and region:

Asia Pacific Cloud Computing Service Outlook (Revenue, USD Billion, 2018 - 2030)

Infrastructure as a service (IaaS)

Platform as a service (PaaS)

Software as a service (SaaS)

Asia Pacific Cloud Computing Deployment Outlook (Revenue, USD Billion, 2018 - 2030)

Public

Private

Hybrid

Asia Pacific Cloud Computing Enterprise Size Outlook (Revenue, USD Billion, 2018 - 2030)

Large Enterprises

Small & Medium Enterprises

Asia Pacific Cloud Computing End-use Outlook (Revenue, USD Billion, 2018 - 2030)

BFSI

IT & Telecom

Retail & Consumer Goods

Manufacturing

Energy & Utilities

Healthcare

Media & Entertainment

Government & Public Sector

Others

Asia Pacific Cloud Computing Regional Outlook (Revenue, USD Billion, 2018 - 2030)

China

Japan

India

Australia

South Korea

Download your FREE sample PDF copy of the Asia Pacific Cloud Computing Market today and explore key data and trends.

[souhaillaghchimdev]

Using Docker in Software Development

Docker has become a vital tool in modern software development. It allows developers to package applications with all their dependencies into lightweight, portable containers. Whether you're building web applications, APIs, or microservices, Docker can simplify development, testing, and deployment.

What is Docker?

Docker is an open-source platform that enables you to build, ship, and run applications inside containers. Containers are isolated environments that contain everything your app needs—code, libraries, configuration files, and more—ensuring consistent behavior across development and production.

Why Use Docker?

Consistency: Run your app the same way in every environment.

Isolation: Avoid dependency conflicts between projects.

Portability: Docker containers work on any system that supports Docker.

Scalability: Easily scale containerized apps using orchestration tools like Kubernetes.

Faster Development: Spin up and tear down environments quickly.

Basic Docker Concepts

Image: A snapshot of a container. Think of it like a blueprint.

Container: A running instance of an image.

Dockerfile: A text file with instructions to build an image.

Volume: A persistent data storage system for containers.

Docker Hub: A cloud-based registry for storing and sharing Docker images.

Example: Dockerizing a Simple Python App

Let’s say you have a Python app called app.py: # app.py print("Hello from Docker!")

Create a Dockerfile: # Dockerfile FROM python:3.10-slim COPY app.py . CMD ["python", "app.py"]

Then build and run your Docker container: docker build -t hello-docker . docker run hello-docker

This will print Hello from Docker! in your terminal.

Popular Use Cases

Running databases (MySQL, PostgreSQL, MongoDB)

Hosting development environments

CI/CD pipelines

Deploying microservices

Local testing for APIs and apps

Essential Docker Commands

docker build -t <name> . — Build an image from a Dockerfile

docker run <image> — Run a container from an image

docker ps — List running containers

docker stop <container_id> — Stop a running container

docker exec -it <container_id> bash — Access the container shell

Docker Compose

Docker Compose allows you to run multi-container apps easily. Define all your services in a single docker-compose.yml file and launch them with one command: version: '3' services: web: build: . ports: - "5000:5000" db: image: postgres

Start everything with:docker-compose up

Best Practices

Use lightweight base images (e.g., Alpine)

Keep your Dockerfiles clean and minimal

Ignore unnecessary files with .dockerignore

Use multi-stage builds for smaller images

Regularly clean up unused images and containers

Conclusion

Docker empowers developers to work smarter, not harder. It eliminates "it works on my machine" problems and simplifies the development lifecycle. Once you start using Docker, you'll wonder how you ever lived without it!

[hawkstack]

Build, Deploy, Scale: Red Hat OpenShift Training That Accelerates Your Career

Red Hat OpenShift has become a cornerstone for enterprises aiming to enhance their cloud-native application deployments. At HawkStack Technologies, we offer comprehensive training programs designed to equip professionals with the skills needed to excel in OpenShift environments.​

Why Choose HawkStack for OpenShift Training?

Expert-Led Instruction: Our courses are delivered by seasoned professionals with extensive experience in OpenShift and Kubernetes.​

Hands-On Learning: We emphasize practical, real-world scenarios to ensure participants can apply their knowledge effectively.​

Flexible Learning Options: Whether you prefer online sessions or in-person classes, we provide flexible training schedules to suit your needs.​

Our OpenShift Training Pathway

Our structured training pathway guides you from foundational concepts to advanced OpenShift administration:​

Red Hat OpenShift I: Containers & Kubernetes (DO180): Build core knowledge in managing containers through hands-on experience with containers, Kubernetes, and the Red Hat OpenShift Container Platform.​

Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280): Configure, manage, and troubleshoot OpenShift clusters and containerized applications.​

Red Hat OpenShift Administration III: Scaling Kubernetes Deployments in the Enterprise (DO380): Focus on the advanced skills needed to operate and manage large-scale OpenShift clusters.​

Each course aligns with Red Hat’s certification exams, enabling you to validate your skills and advance your career.

Upcoming Training Batches

We regularly launch new training cohorts. For instance, our upcoming batch for Red Hat OpenShift Administration III is scheduled to commence soon. Seats are limited, so early enrollment is recommended.​

Enroll Today

Advance your career with HawkStack’s Red Hat OpenShift training. Visit our www.hawkstack.com to learn more and register for upcoming sessions.​

[khushidw]

How to Scale a Node.js Application for High Performance

Scaling a Node.js application is essential for handling high traffic, large user bases, and increasing workloads efficiently. To achieve high performance and scalability, businesses must implement the right optimization techniques, load balancing, and cloud-based solutions.

Key Strategies to Scale a Node.js Application:

Use Load Balancing – Distribute incoming requests across multiple instances using NGINX, HAProxy, or AWS Elastic Load Balancer.

Implement Caching – Optimize performance with Redis, Memcached, or CDN caching for static files and frequently accessed data.

Optimize Database Performance – Use NoSQL databases (MongoDB, Cassandra) or SQL sharding and indexing to improve data retrieval speed.

Utilize Microservices Architecture – Break monolithic applications into microservices for better scalability and maintainability.

Leverage Auto-Scaling & Containerization – Deploy Docker & Kubernetes to manage instances dynamically based on traffic loads.

Use Asynchronous Processing – Implement message queues (RabbitMQ, Kafka) or worker threads for non-blocking operations.

Optimize Code & Reduce Latency – Minimize blocking operations, optimize event loops, and use Node.js clustering for multi-core processing.

Monitor & Optimize Performance – Use APM tools like New Relic, Prometheus, or Datadog to track and enhance application efficiency.

[vatt-world]

h

Technical Skills (Java, Spring, Python)

Q1: Can you walk us through a recent project where you built a scalable application using Java and Spring Boot? A: Absolutely. In my previous role, I led the development of a microservices-based system using Java with Spring Boot and Spring Cloud. The app handled real-time financial transactions and was deployed on AWS ECS. I focused on building stateless services, applied best practices like API versioning, and used Eureka for service discovery. The result was a 40% improvement in performance and easier scalability under load.

Q2: What has been your experience with Python in data processing? A: I’ve used Python for ETL pipelines, specifically for ingesting large volumes of compliance data into cloud storage. I utilized Pandas and NumPy for processing, and scheduled tasks with Apache Airflow. The flexibility of Python was key in automating data validation and transformation before feeding it into analytics dashboards.

Cloud & DevOps

Q3: Describe your experience deploying applications on AWS or Azure. A: Most of my cloud experience has been with AWS. I’ve deployed containerized Java applications to AWS ECS and used RDS for relational storage. I also integrated S3 for static content and Lambda for lightweight compute tasks. In one project, I implemented CI/CD pipelines with Jenkins and CodePipeline to automate deployments and rollbacks.

Q4: How have you used Docker or Kubernetes in past projects? A: I've containerized all backend services using Docker and deployed them on Kubernetes clusters (EKS). I wrote Helm charts for managing deployments and set up autoscaling rules. This improved uptime and made releases smoother, especially during traffic spikes.

Collaboration & Agile Practices

Q5: How do you typically work with product owners and cross-functional teams? A: I follow Agile practices, attending sprint planning and daily stand-ups. I work closely with product owners to break down features into stories, clarify acceptance criteria, and provide early feedback. My goal is to ensure technical feasibility while keeping business impact in focus.

Q6: Have you had to define technical design or architecture? A: Yes, I’ve been responsible for defining the technical design for multiple features. For instance, I designed an event-driven architecture for a compliance alerting system using Kafka, Java, and Spring Cloud Streams. I created UML diagrams and API contracts to guide other developers.

Testing & Quality

Q7: What’s your approach to testing (unit, integration, automation)? A: I use JUnit and Mockito for unit testing, and Spring’s Test framework for integration tests. For end-to-end automation, I’ve worked with Selenium and REST Assured. I integrate these tests into Jenkins pipelines to ensure code quality with every push.

Behavioral / Cultural Fit

Q8: How do you stay updated with emerging technologies? A: I subscribe to newsletters like InfoQ and follow GitHub trending repositories. I also take part in hackathons and complete Udemy/Coursera courses. Recently, I explored Quarkus and Micronaut to compare their performance with Spring Boot in cloud-native environments.

Q9: Tell us about a time you challenged the status quo or proposed a modern tech solution. A: At my last job, I noticed performance issues due to a legacy monolith. I advocated for a microservices transition. I led a proof-of-concept using Spring Boot and Docker, which gained leadership buy-in. We eventually reduced deployment time by 70% and improved maintainability.

Bonus: Domain Experience

Q10: Do you have experience supporting back-office teams like Compliance or Finance? A: Yes, I’ve built reporting tools for Compliance and data reconciliation systems for Finance. I understand the importance of data accuracy and audit trails, and have used role-based access and logging mechanisms to meet regulatory requirements.

[hiringjournal]

How Much Does It Cost to Hire a Kubernetes Developer?

Kubernetes has evolved as a major part of the modern cloud infrastructure helping your tech business to manage containerized applications at scale. From deployment automation to ensuring high availability, Kubernetes has become a vital skill for DevOps and cloud teams. 

As the adaptation of container orchestration is expanding among businesses, the demand to hire software engineers skilled in Kubernetes is on the rise. If you too are one among this race you must know what you should pay these experts and what factors influence the cost decision. 

Let’s explore the answer to these cost considerations and how you can get the best value for your budget. 

What Impacts the Cost of Hiring a Kubernetes Developer?

Hiring Kubernetes developers can cost different amounts depending on a number of important factors:

Experience level: DevOps and cloud certifications (such as CKA or AWS Certified) are more expensive for senior engineers.

Location: Generally speaking, developers in Western Europe and North America charge more than those in Asia or Eastern Europe.

Type of employment: There are differences in pricing between contractors, full-time workers, and freelancers.

Project complexity: Support for basic Kubernetes deployment will be less expensive than a complete cloud migration.

A rookie Kubernetes engineer, for instance, may make between $60,000 and $90,000 a year, but senior-level talent in the US might fetch $130,000 to $180,000 or more. You can utilize salary benchmarking tools to get real-time data on the salary trends in the market for a specific skill. 

Freelance vs. Full-Time vs. Contract Developers

The total cost is impacted by your hiring process. Here’s a comparison between the top 3 hiring approaches:

Freelancers: Economical for brief assignments or minor jobs. Depending on experience, rates range from $50 to $120 per hour.

Full-time workers are ideal for long-term infrastructure requirements. This includes pay, perks, and the cost of onboarding.

You can choose to hire through an IT staffing company for prompt access to qualified personnel. Without a drawn-out hiring process, agencies assist you in finding the ideal candidate.

If you want to skip a drawn-out hiring process or need talent on-hand, many tech organizations turn to IT staffing agencies.

Cost-Saving Tips Without Compromising Quality

To properly control expenses when you hire Kubernetes developers:

Take into account remote or offshore hiring options. 

Employ a hybrid approach that combines in-house support with a part-time Kubernetes specialist. 

For flexible contracts and pre-screened talent, collaborate with an IT staffing company.

To stay competitive and prevent overpaying, use tools for benchmarking salaries.

Why Kubernetes Developers Are Worth the Investment

Kubernetes specialists provide significant ROI, despite their high cost. They assist to:

Increase the dependability of the app

Cut down on downtime

Automate deployment and scaling

Protect your infrastructure.

Hiring a Kubernetes developer can have a direct influence on system stability and speed-to-market for rapidly expanding IT businesses.

Summing Up

Your desired hiring methodology, experience level, and geography all affect how much it costs to hire Kubernetes developers. Cost and skill must be balanced whether you work for an IT staffing company, as a freelancer, or full-time. Additionally, Kubernetes developers become an essential component of your cloud success when combined with the appropriate group of software engineers. As a modern tech company, that counts as your money being well invested.