In today’s rapidly evolving technological landscape, businesses face increasing pressure to maintain high-performing applications. Every downtime or performance issue not only disrupts service but also impacts the user experience and, ultimately, the bottom line. As applications become more complex—spanning cloud environments, microservices, and hybrid systems—managing and monitoring performance becomes increasingly challenging. This is where AppDynamics, a comprehensive Application Performance Management (APM) solution, plays a critical role.

AppDynamics helps organizations gain deep visibility into their applications, infrastructure, and end-user experiences by continuously monitoring and managing performance. With real-time insights and powerful analytics, AppDynamics empowers IT teams to proactively identify performance bottlenecks, resolve issues before they impact users, and ensure optimal performance across the entire application stack. In this blog, we will explore what AppDynamics is, its use cases, core features, architecture, and installation process, and provide you with a basic tutorial to get started.

What is AppDynamics?

AppDynamics is an advanced Application Performance Management (APM) tool designed to monitor, optimize, and troubleshoot the performance of applications, databases, and infrastructure. In a modern digital ecosystem where apps run across multiple platforms—cloud, on-premise, hybrid, or microservices architectures—AppDynamics provides a unified, end-to-end solution to track everything from the user interface to the backend servers.

Through its advanced analytics engine, AppDynamics delivers real-time insights into the health of applications, helping businesses detect performance bottlenecks, track user interactions, and optimize infrastructure resources. AppDynamics supports a wide range of programming languages (including Java, .NET, Node.js, PHP, and Python) and can seamlessly integrate with cloud platforms like AWS, Microsoft Azure, and Google Cloud, making it an essential tool for organizations seeking to maintain and optimize the performance of their applications, regardless of their infrastructure environment.

Top 10 Use Cases of AppDynamics:

1. Application Performance Monitoring (APM): AppDynamics provides real-time monitoring of applications, ensuring that performance is tracked constantly. It identifies slow response times, errors, and bottlenecks in transactions, offering visibility into how applications perform under different conditions. This is especially important for mission-critical applications where even a minor delay could affect users and business operations.
2. Real-Time Troubleshooting and Diagnostics: One of the most valuable aspects of AppDynamics is its ability to provide real-time insights. IT teams can identify issues as they arise and quickly trace the root cause of performance problems. Whether it’s a slow database query, a memory leak, or an external API failure, AppDynamics helps isolate and resolve problems swiftly to minimize downtime.
3. End-User Experience Monitoring (EUM): AppDynamics tracks user interactions with applications, providing insights into the end-user experience. This includes monitoring page load times, interaction delays, and crashes, helping businesses optimize user experiences and ensure they meet performance expectations. With this data, organizations can adjust their apps to deliver smoother and faster user journeys.
4. Cloud Monitoring: As organizations move to the cloud, ensuring the performance of cloud-based applications becomes increasingly complex. AppDynamics seamlessly integrates with cloud platforms such as AWS, Google Cloud, and Azure, providing visibility into cloud-hosted services, virtualized environments, and containerized applications.
5. Business Transaction Monitoring: AppDynamics tracks critical business transactions end-to-end. This allows organizations to monitor vital interactions such as customer purchases, data transfers, or API calls, which directly affect business revenue and customer satisfaction. By analyzing these transactions, businesses can identify areas of improvement and ensure that business-critical processes run smoothly.
6. Synthetic Monitoring: In addition to monitoring live user interactions, AppDynamics offers synthetic monitoring, which simulates user actions to test the application’s performance from various locations. This proactive approach helps businesses catch performance issues before real users experience them, reducing the risk of customer dissatisfaction.
7. Microservices and Container Monitoring: With the rise of microservices and containers, monitoring has become more complex. AppDynamics provides robust support for monitoring microservices, Kubernetes, Docker, and other containerized applications, helping teams track performance across these dynamic environments.
8. Database Performance Monitoring: AppDynamics offers in-depth visibility into database performance, helping businesses track query execution times, identify slow queries, and monitor database response times. By optimizing database performance, organizations can prevent application bottlenecks that are often caused by inefficient database queries.
9. Root Cause Analysis and Diagnostics: When performance issues arise, AppDynamics automatically traces business transactions across various tiers of an application, allowing teams to quickly pinpoint the root cause of the problem. Whether it’s a network issue, server misconfiguration, or faulty application code, AppDynamics accelerates the identification of the problem and streamlines the resolution process.
10. Compliance Monitoring: AppDynamics also helps businesses meet compliance and regulatory requirements by tracking data flows, ensuring that applications are operating within set performance thresholds, and maintaining performance standards that meet industry regulations (such as GDPR, HIPAA, and PCI DSS).

What are the Features of AppDynamics?

AppDynamics offers a wide range of features designed to help businesses gain complete visibility into their application’s performance. Here’s a breakdown of some of its key features:

1. Real-Time Application Monitoring: AppDynamics provides continuous monitoring of application performance and delivers real-time data about application health, allowing businesses to address issues as soon as they occur.
2. End-to-End Transaction Tracking: The platform tracks every business transaction from start to finish, offering visibility into how transactions flow across the application stack. This helps businesses identify and fix issues affecting critical processes.
3. Custom Dashboards: Users can create custom dashboards to monitor key performance indicators (KPIs) and track critical metrics. These dashboards help businesses stay on top of application performance at a glance.
4. Root Cause Diagnostics: When a problem arises, AppDynamics automatically traces the transaction journey and pinpoints the root cause, helping IT teams quickly fix performance bottlenecks without wasting time on guesswork.
5. Cloud Monitoring: AppDynamics seamlessly integrates with cloud environments, providing comprehensive visibility into cloud-based applications and ensuring optimal performance in dynamic, hybrid cloud environments.
6. Business and Infrastructure Analytics: The platform provides both business transaction and infrastructure monitoring, allowing businesses to understand how application performance impacts business goals and how infrastructure resources are utilized.
7. Alerts and Automation: AppDynamics allows users to set up alerts based on performance thresholds. When performance drops below acceptable levels, AppDynamics can notify the team immediately, ensuring that issues are addressed proactively.
8. Database Monitoring: It offers in-depth database performance analysis, allowing businesses to identify slow queries and optimize their database resources for better performance.
9. Synthetic and Real User Monitoring: The combination of synthetic monitoring and real-user monitoring helps businesses ensure that their applications perform optimally, both in test environments and in production.
10. Application Mapping and Visualization: AppDynamics automatically maps applications to give teams a visual representation of how transactions flow through the system. This allows users to quickly identify where issues occur within the application.

How AppDynamics Works and Architecture?

AppDynamics operates on a client-server architecture designed to monitor applications, databases, and infrastructure components. Here’s a high-level look at how it works:

1. AppDynamics Controller: The Controller is the heart of the system, storing all monitoring data and providing actionable insights. It processes data collected from agents and visualizes it in dashboards, reports, and real-time alerts.
2. AppDynamics Agents: AppDynamics uses lightweight agents deployed on application servers (Java, .NET, Node.js, PHP, etc.) to monitor the application’s performance. These agents collect real-time performance data and communicate it back to the Controller.
3. Transaction Analytics: AppDynamics analyzes transactions from end to end, tracking the interaction between application components, databases, APIs, and services. By understanding how these transactions flow, it helps businesses identify bottlenecks and optimize processes.
4. Data Analytics Engine: The analytics engine processes the data from the agents, providing businesses with insights into trends, root causes of issues, and recommendations for improvement.
5. Dashboards: Interactive dashboards provide an intuitive interface to visualize the health of applications, business transactions, and infrastructure components in real-time.

How to Install AppDynamics?

Here’s a concise guide on how to install AppDynamics:

Steps to Install AppDynamics:

1. Sign Up for AppDynamics:
   • Go to the AppDynamics website and sign up for an account or request a free trial.
2. Download the AppDynamics Agent:
   • Once logged in, navigate to the “Get Started” section or the Downloads page.
   • Select the appropriate agent based on your environment (Java, .NET, Node.js, Python, etc.).
   • Download the AppDynamics agent for your server or cloud environment.
3. Install the Agent:
   • For Java Applications:
     • Unzip the downloaded agent package.
     • Add the following Java Virtual Machine (JVM) argument to your application’s startup script:

         -javaagent:/path/to/agent.jar

1. For Other Environments (e.g., .NET, Node.js, etc.):
   • Follow the specific instructions provided by AppDynamics for the type of agent you are installing. The setup typically involves adding environment variables or modifying configuration files.
2. Configure the Agent:
   • After installing, configure the agent to point to your AppDynamics controller (which handles data collection and analysis). You’ll need to provide the Controller Host, Port, and Application Name.
3. Verify the Installation:
   • Restart your application with the agent enabled.
   • Log in to your AppDynamics account and go to the Applications dashboard.
   • Check if the application is listed and data is being collected (e.g., response times, error rates, etc.).
4. Access the Dashboard:
   • You can view the performance metrics, including transaction tracking, infrastructure monitoring, and real-time user insights, through the AppDynamics web interface.

Additional Setup (Optional):

• Configure Alerts and Dashboards: Set up custom alerts and dashboards to monitor specific metrics that are crucial for your business.

Basic Tutorials of AppDynamics: Getting Started

1. Getting Familiar with the Dashboard: Once you’ve installed AppDynamics and connected your applications, take the time to explore the dashboard. The dashboard offers a variety of metrics, including response times, error rates, and system load.
2. Create Custom Dashboards: Learn how to set up custom dashboards that show critical performance data for your applications. These dashboards allow you to track metrics relevant to your business and IT needs.
3. Set Up Alerts: Set thresholds for key performance indicators (KPIs) and configure alerts to notify your team when performance issues arise.
4. Monitor Key Business Transactions: Define your business-critical transactions and monitor their performance in real time.
5. Perform Root Cause Analysis: Use AppDynamics’ automated root cause analysis to detect and resolve performance issues quickly.
6. Monitor Microservices and Cloud Environments: Learn how to monitor microservices and cloud platforms like AWS and Azure to ensure that all your modern applications are functioning as expected.

The post What is AppDynamics and Use Cases of AppDynamics? appeared first on Artificial Intelligence.

As businesses adopt cloud-native applications, microservices, and DevOps, managing Kubernetes environments efficiently becomes crucial. Red Hat OpenShift is an enterprise-grade Kubernetes platform that simplifies container orchestration, security, automation, and hybrid cloud deployments.

OpenShift extends Kubernetes capabilities with enhanced security, developer-friendly workflows, and automation features, making it an ideal container platform for enterprises.

This blog will explore what Red Hat OpenShift is, its key use cases, features, architecture, installation process, and step-by-step tutorials for getting started.

What is Red Hat OpenShift?

Red Hat OpenShift is a fully managed, enterprise Kubernetes platform that provides a secure, scalable, and automated environment for running containerized applications. It is based on Kubernetes but adds security, automation, developer tools, and operational consistency across on-premises, cloud, and hybrid environments.

Why OpenShift?

• Enterprise Kubernetes with built-in security, automation, and compliance.
• Hybrid and Multi-Cloud Compatibility with support for AWS, Azure, Google Cloud, and on-premise data centers.
• CI/CD Integration for faster application deployment and DevOps enablement.
• Developer-Centric Features with support for OpenShift Pipelines, Operators, and Helm charts.

OpenShift enables enterprises to manage Kubernetes clusters at scale while ensuring compliance, governance, and developer productivity.

Top 10 Use Cases of Red Hat OpenShift

1. Enterprise Kubernetes Orchestration

OpenShift provides enterprise-grade Kubernetes with security policies, role-based access control (RBAC), and networking solutions to manage workloads efficiently.

2. Hybrid & Multi-Cloud Deployments

OpenShift runs seamlessly across public clouds (AWS, Azure, GCP), private data centers, and hybrid cloud environments, providing a consistent platform.

3. Microservices and Cloud-Native Applications

OpenShift simplifies microservices development by providing containers, Istio service mesh, and Operators to manage application lifecycles.

4. DevOps & Continuous Integration/Continuous Deployment (CI/CD)

With OpenShift Pipelines (Tekton), Jenkins, and ArgoCD, OpenShift supports automated deployments, testing, and rollbacks.

5. Artificial Intelligence & Machine Learning (AI/ML)

OpenShift integrates with TensorFlow, Kubeflow, and Jupyter Notebooks to enable AI/ML model training and deployment.

6. Edge Computing & IoT

Lightweight OpenShift clusters can run on edge devices and remote locations, supporting 5G, IoT, and low-latency applications.

7. Security and Compliance

OpenShift provides built-in security policies, RBAC, SELinux enforcement, and automated compliance audits for enterprises needing SOC2, PCI-DSS, HIPAA, or GDPR compliance.

8. Stateful Applications & Database Management

Unlike traditional Kubernetes, OpenShift has persistent storage support for databases like MySQL, PostgreSQL, MongoDB, and Redis.

9. Serverless Computing with Knative

OpenShift supports Knative-based serverless workloads, enabling developers to run event-driven applications with minimal resource usage.

10. Kubernetes-as-a-Service (KaaS)

OpenShift allows organizations to offer self-service Kubernetes clusters to developers with governance and security controls.

What Are the Features of Red Hat OpenShift?

1. Enterprise-Ready Kubernetes

• Built on Kubernetes with added security, automation, and support for production workloads.

2. Developer-Focused Experience

• Provides a self-service developer portal with Red Hat CodeReady Workspaces, Helm charts, and OpenShift Pipelines (Tekton).

3. Security & Compliance

• Built-in RBAC, Security Context Constraints (SCC), SELinux, compliance scanning, and policy enforcement.

4. OpenShift Pipelines & GitOps

• Supports Tekton CI/CD pipelines and ArgoCD GitOps workflows for automated deployments.

5. OpenShift Operators

• Automates deployment, scaling, and management of applications and services.

6. Multi-Cloud & Hybrid Cloud Support

• Runs seamlessly on on-prem, AWS, Azure, Google Cloud, and OpenStack.

7. Networking & Service Mesh

• Supports OpenShift SDN, Istio service mesh, and Calico networking for high-performance Kubernetes networking.

8. Automated Scaling & Load Balancing

• Uses Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler to manage workloads efficiently.

9. Persistent Storage for Stateful Applications

• Supports Ceph, GlusterFS, AWS EBS, Azure Disks, Google Persistent Disks.

10. Monitoring & Logging

• Integrates with Prometheus, Grafana, and Elasticsearch for Kubernetes observability and logging.

How Red Hat OpenShift Works and Architecture

How OpenShift Works

Red Hat OpenShift extends Kubernetes with added security, automation, and developer tools, making it easier to deploy and manage containerized applications at scale.

Red Hat OpenShift Architecture

1. OpenShift Control Plane (Master Nodes)

• API Server: Handles communication between OpenShift components.
• Controller Manager: Manages cluster lifecycle events.
• etcd: Stores Kubernetes cluster data.
• Scheduler: Assigns workloads to worker nodes.

2. Worker Nodes (Compute Nodes)

• Kubelet: Manages container execution.
• CRI-O: OpenShift’s lightweight container runtime.
• SDN & Service Mesh: Provides networking and service-to-service communication.

3. OpenShift Platform Services

• Authentication & RBAC: Manages user access and security policies.
• Logging & Monitoring: Uses Prometheus and Elasticsearch for observability.

How to Install Red Hat OpenShift

Installation Methods

• OpenShift Local (CRC) for development
• OpenShift on Bare Metal or Virtual Machines
• OpenShift on Public Cloud (AWS, Azure, GCP)

Installing OpenShift Using CRC (For Local Development)

Step 1: Download OpenShift CRC

curl -LO https://mirror.openshift.com/pub/openshift-v4/clients/crc/latest/crc-linux-amd64.tar.xz

Step 2: Extract and Install CRC

tar -xvf crc-linux-amd64.tar.xz
sudo mv crc /usr/local/bin/

Step 3: Start OpenShift Cluster

crc setup
crc start

Step 4: Access OpenShift Web Console

crc console

Basic Tutorials of Red Hat OpenShift: Getting Started

1. Deploying an Application on OpenShift

oc new-app nginx --name=myapp

• Deploys an Nginx web server inside an OpenShift cluster.

2. Exposing a Service (Ingress / Route)

oc expose svc myapp --port=80 --type=NodePort

• Makes the application accessible via external routes.

3. Scaling an Application

oc scale deployment myapp --replicas=5

• Increases the number of pods for the application.

4. Deploying a Helm Chart

helm install mychart bitnami/nginx

• Deploys an Nginx server using Helm charts.

5. Checking Running Pods

oc get pods

The post What is Red Hat OpenShift and Its Use Cases? appeared first on Artificial Intelligence.

In today’s software development world, containerization has revolutionized the way applications are built, deployed, and managed. Docker is the most popular containerization platform, enabling developers and organizations to create, run, and manage applications in isolated environments. It eliminates the traditional “works on my machine” problem by ensuring consistency across multiple environments.

Docker is widely used in DevOps, CI/CD pipelines, microservices architectures, cloud-native development, and more. This blog will explore what Docker is, its top use cases, key features, architecture, installation process, and a step-by-step guide to getting started.

What is Docker?

Docker is an open-source platform that allows developers to build, package, and run applications in lightweight, portable containers. A Docker container is a standardized unit of software that includes everything needed to run an application: code, runtime, libraries, dependencies, and configurations.

Why Use Docker?

• Portability: Containers can run on any system with Docker installed.
• Consistency: Ensures identical application behavior across environments (development, testing, production).
• Isolation: Runs applications in isolated environments, avoiding conflicts.
• Efficiency: Uses fewer resources compared to traditional virtual machines (VMs).

How Docker is Different from Virtual Machines (VMs)?

Docker makes application deployment faster, more scalable, and cost-effective by simplifying how software is packaged and shipped.

Top 10 Use Cases of Docker

1. Application Containerization

Docker encapsulates applications with all their dependencies, ensuring they run identically in any environment.

2. Microservices Architecture

Docker is ideal for breaking down monolithic applications into microservices, allowing teams to develop, deploy, and scale components independently.

3. Continuous Integration & Continuous Deployment (CI/CD)

Docker integrates seamlessly with Jenkins, GitHub Actions, GitLab CI/CD, enabling fast, automated software releases.

4. Cloud-Native Development

Docker works across AWS, Azure, Google Cloud, making it easy to deploy applications in hybrid, multi-cloud, and Kubernetes environments.

5. Simplified Development and Testing

Developers can use Docker Compose to create isolated development environments, reducing conflicts between dependencies.

6. Automated Scaling & Orchestration

With Docker Swarm or Kubernetes, applications can be automatically scaled up or down based on demand.

7. Big Data and Machine Learning

Docker simplifies the deployment of AI/ML frameworks like TensorFlow, PyTorch, and Apache Spark by packaging dependencies in containers.

8. Edge Computing and IoT

Docker runs lightweight containers on IoT devices and edge servers, optimizing compute resources.

9. Database Containerization

Databases like MySQL, PostgreSQL, and MongoDB can be containerized, making them easier to manage and scale.

10. Legacy Application Modernization

Organizations can move legacy applications into containers without rewriting the entire codebase, extending their lifespan.

What Are the Features of Docker?

1. Containerization

Docker provides an efficient way to package and isolate applications along with their dependencies.

2. Portability

Containers run consistently across different environments, from a developer’s laptop to production servers.

3. Version Control & Rollbacks

Docker allows versioning of images, enabling easy rollbacks to previous states.

4. Lightweight & Fast

Containers use less CPU and memory compared to traditional VMs.

5. Multi-Platform Support

Docker runs on Windows, macOS, Linux, and cloud environments.

6. Security & Isolation

Each container runs in its own isolated environment, improving security.

7. Docker Compose

Defines and runs multi-container applications using a simple YAML configuration file.

8. Built-in Networking

Docker allows seamless container-to-container communication.

9. Integration with DevOps Tools

Supports popular tools like Kubernetes, Terraform, Jenkins, GitHub Actions.

10. Scalable and Flexible

Works in both single-node setups and large-scale deployments with Kubernetes.

How Docker Works and Architecture

1. Docker Engine

• The core of Docker, is responsible for running containers.
• Consists of Docker Daemon, CLI, and REST API.

2. Docker Images

• Read-only templates that define how containers should run.
• Created using Dockerfiles.

3. Docker Containers

• Running instances of Docker Images.

4. Docker Hub

• Public/private registry to store and share Docker images.

5. Docker Compose

• Tool for managing multi-container applications.

6. Container Orchestration

• Docker Swarm and Kubernetes help manage large-scale container deployments.

How to Install Docker

Installing Docker on Linux

sudo apt update
sudo apt install docker.io -y
sudo systemctl start docker
sudo systemctl enable docker

Installing Docker on macOS (via Homebrew)

brew install --cask docker

Installing Docker on Windows

1. Download Docker Desktop from Docker’s official website.
2. Follow the installation wizard and restart your system.

Verify Docker Installation

docker --version
docker run hello-world

Basic Tutorials of Docker: Getting Started

1. Running a Simple Container

docker run -d -p 8080:80 nginx

• This pulls and runs an Nginx web server container on port 8080.

2. Listing Running Containers

docker ps

3. Stopping a Running Container

docker stop <container_id>

4. Removing a Container

docker rm <container_id>

5. Pulling an Image from Docker Hub

docker pull mysql

6. Creating a Custom Docker Image

1. Create a Dockerfile: FROM python:3.9 COPY app.py /app/app.py WORKDIR /app CMD ["python", "app.py"]
2. Build and run the image: docker build -t my-python-app . docker run my-python-app

7. Running Multiple Containers with Docker Compose

1. Create docker-compose.yml: version: '3' services: web: image: nginx ports: - "8080:80" database: image: postgres
2. Run with: docker-compose up -d

8. Viewing Container Logs

docker logs <container_id>

9. Executing Commands Inside a Running Container

docker exec -it <container_id> bash

10. Removing All Containers and Images

docker system prune -a

The post What is Docker and Use Cases of Docker? appeared first on Artificial Intelligence.

As modern applications become more complex and distributed, managing containerized workloads efficiently is critical for scalability, reliability, and performance. Kubernetes, often abbreviated as K8s, is the industry-leading open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Originally developed by Google and now maintained by the Cloud Native Computing Foundation (CNCF), Kubernetes has become the standard for managing cloud-native applications.

Kubernetes eliminates many of the challenges associated with manually deploying and managing containers across distributed environments. It provides organizations with the agility, flexibility, and automation required to run containerized applications seamlessly across on-premises, cloud, and hybrid environments.

In this blog, we will explore what Kubernetes is, its top use cases, features, architecture, installation process, and a step-by-step guide to getting started.

What is Kubernetes?

Kubernetes is an open-source container orchestration platform designed to manage containerized workloads and services. It provides automation for deployment, scaling, networking, and storage for applications running in containers.

Key Characteristics of Kubernetes:

• Automated container orchestration: Eliminates manual efforts in deploying and managing containers.
• Self-healing capabilities: Restarts failed containers and reschedules workloads automatically.
• Scalability: Allows horizontal scaling of applications based on demand.
• Multi-cloud compatibility: Runs on AWS, Azure, Google Cloud, and on-premises environments.
• Declarative Configuration: Uses YAML files to define infrastructure as code (IaC).

Why Kubernetes?

Before Kubernetes, organizations relied on traditional virtual machines (VMs) or bare-metal servers, leading to resource inefficiencies. Kubernetes provides an efficient way to deploy, manage, and scale applications without worrying about infrastructure constraints.

With Kubernetes, developers can: ✔ Deploy applications faster
✔ Scale up or down automatically
✔ Manage application failures with self-healing mechanisms
✔ Optimize resource usage

Top 10 Use Cases of Kubernetes

1. Container Orchestration

Kubernetes automates container deployment, management, and scaling, reducing manual intervention in distributed applications.

2. Microservices Management

Kubernetes simplifies the management of microservices-based applications, ensuring seamless communication between services and optimizing resource allocation.

3. Hybrid and Multi-Cloud Deployments

With Kubernetes, businesses can run applications across multiple cloud providers (AWS, Azure, GCP) and on-premises environments with minimal configuration changes.

4. Auto-Scaling Applications

Kubernetes automatically scales applications up or down based on CPU, memory, or custom-defined metrics using the Horizontal Pod Autoscaler (HPA).

5. CI/CD Automation for DevOps

Kubernetes integrates with Jenkins, GitLab CI/CD, and ArgoCD to enable continuous integration and continuous deployment (CI/CD) pipelines.

6. Big Data & AI/ML Workloads

Kubernetes manages Big Data analytics, AI/ML model training, and processing using frameworks like TensorFlow, Apache Spark, and Jupyter notebooks.

7. Serverless Computing

With Kubernetes-based serverless frameworks like Knative and OpenFaaS, developers can run event-driven applications without managing infrastructure.

8. Disaster Recovery and High Availability

Kubernetes ensures fault tolerance by automatically replacing failed containers and replicating workloads across multiple nodes for high availability.

9. IoT and Edge Computing

Kubernetes is used for deploying containerized workloads on IoT devices and edge environments, ensuring seamless operation across distributed systems.

10. Multi-Tenant SaaS Applications

Kubernetes supports multi-tenancy, allowing SaaS providers to run multiple customer applications in an isolated and secure environment.

What Are the Features of Kubernetes?

Kubernetes provides a robust set of features that make it a powerful container orchestration platform:

1. Automated Deployments and Rollbacks

• Kubernetes enables rolling updates and rollbacks, ensuring smooth deployment without downtime.

2. Self-Healing Mechanism

• Automatically restarts failed containers.
• Replaces unhealthy nodes or pods.
• Reschedules workloads to healthy nodes.

3. Horizontal & Vertical Scaling

• Horizontal Pod Autoscaler (HPA) dynamically scales applications based on demand.
• Vertical Pod Autoscaler (VPA) adjusts resource allocations for efficient CPU and memory usage.

4. Load Balancing and Service Discovery

• Kubernetes provides built-in service discovery and load balancing through Services and Ingress controllers.

5. Multi-Cloud and Hybrid Support

• Run workloads across on-premises, cloud, and hybrid environments seamlessly.

6. Secrets and Config Management

• Kubernetes securely manages secrets, environment variables, and configuration data.

7. Networking and Service Mesh

• Supports Kubernetes-native networking, enabling seamless communication between containers.
• Works with Istio, Linkerd, and Consul for service mesh implementation.

8. Persistent Storage Management

• Integrates with AWS EBS, Azure Disks, Google Persistent Disks, and on-prem storage.

9. Role-Based Access Control (RBAC)

• Implements fine-grained access controls for securing cluster resources.

10. Observability and Monitoring

• Works with Prometheus, Grafana, and ELK Stack for monitoring and logging.

How Kubernetes Works and Architecture

Kubernetes Architecture Overview

Kubernetes follows a master-worker node architecture to manage containers efficiently.

1. Master Node (Control Plane)
   • API Server: Manages communication between components.
   • Scheduler: Assigns workloads to worker nodes.
   • Controller Manager: Manages cluster state and ensures desired configurations.
   • etcd: Stores cluster configuration and metadata.
2. Worker Nodes
   • Kubelet: Agent running on each node to manage container execution.
   • Kube Proxy: Handles network communication.
   • Container Runtime (Docker/Containerd): Runs containerized applications.
   • Pods: The smallest deployable unit containing one or more containers.

How to Install Kubernetes

Kubernetes can be installed in multiple ways, including Minikube, kubeadm, managed Kubernetes (EKS, AKS, GKE), or on-prem setups.

Installing Kubernetes using Minikube (For Local Development)

Step 1: Install Minikube

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube

Step 2: Start Kubernetes Cluster

minikube start

Step 3: Verify Kubernetes Installation

kubectl cluster-info
kubectl get nodes

Installing Kubernetes using kubeadm (For Production)

Step 1: Install kubeadm, kubectl, and kubelet

sudo apt update && sudo apt install -y kubeadm kubelet kubectl

Step 2: Initialize Kubernetes Cluster

sudo kubeadm init

Step 3: Configure kubectl

mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config

Step 4: Join Worker Nodes

kubeadm join <master-node-ip>:6443 --token <token> --discovery-token-ca-cert-hash sha256:<hash>

Basic Tutorials of Kubernetes: Getting Started

1. Deploying a Sample Application

kubectl create deployment nginx --image=nginx
kubectl expose deployment nginx --port=80 --type=NodePort

2. Scaling Applications

kubectl scale deployment nginx --replicas=5

3. Viewing Running Pods

kubectl get pods -o wide

4. Deleting a Deployment

kubectl delete deployment nginx

The post What is Kubernetes and Its Use Cases? appeared first on Artificial Intelligence.

Docker has become an essential tool for developers in recent years, and there are many reasons why every developer should learn it. Here are just a few of the benefits:

1. Isolation and consistency: Docker containers provide a self-contained environment for applications, ensuring that they run consistently across different development environments and production servers. This isolation also helps to prevent conflicts between applications and makes it easier to debug problems.
2. Portability: Docker images are lightweight and portable, making it easy to deploy applications to any environment, including cloud platforms, on-premises servers, and even local machines. This portability can significantly reduce the time and effort required to deploy and manage applications.
3. Scalability: Docker containers can be easily scaled up or down, making it easy to handle fluctuating workloads and traffic. This scalability is essential for modern applications that need to be able to handle sudden spikes in demand.
4. Collaboration: Docker images can be shared between developers, making it easy to collaborate on projects and ensure that everyone is working with the same code and dependencies. This can help to improve productivity and reduce the risk of errors.
5. Continuous integration and delivery (CI/CD): Docker can be used to streamline the CI/CD process, making it easier to automate the building, testing, and deployment of applications. This can help to improve the quality of software and reduce the time it takes to get new features to users.
6. Faster deployment and version control: Docker allows for quick and easy deployment of applications. Containerized applications can be launched in seconds, reducing deployment time significantly. Additionally, version control becomes easier as Docker images can be tagged, allowing developers to roll back to previous versions if needed.
7. Microservices architecture: Docker is widely used in microservices-based architecture. With Docker, developers can split their applications into smaller, decoupled services, allowing for better scalability, maintainability, and fault tolerance.

Overall, Docker is a powerful and versatile tool that can make the life of a developer much easier. If you are not already using Docker, I encourage you to learn it today. You will be glad you did.

The post Reasons Why Every Developer Should Learn Docker appeared first on Artificial Intelligence.

Aggregator

Aggregator is a basic web page which invokes various services to get the required information or achieve the required functionality. 

The Aggregate Design Pattern is based on the DRY principle. Based on this principle, you can abstract the logic into a composite microservices and aggregate that particular business logic into one service.

So, for example, if you consider two services: Service A and B, then you can individually scale these services simultaneously by providing the data to the composite microservice.

API Gateway

API Gateway also acts as the entry point for all the microservices and creates fine-grained APIs’ for different types of clients.

With the help of the API Gateway design pattern, the API gateways can convert the protocol request from one type to other. Similarly, it can also offload the authentication/authorization responsibility of the microservice.

So, once the client sends a request, these requests are passed to the API Gateway which acts as an entry point to forward the clients’ requests to the appropriate microservices. Then, with the help of the load balancer, the load of the request is handled and the request is sent to the respective services.

Chained or Chain of Responsibility

Chained or Chain of Responsibility Design Patterns produces a single output which is a combination of multiple chained outputs. So, if you have three services lined up in a chain, then, the request from the client is first received by Service A. Then, this service communicates with the next Service B and collects data. Finally, the second service communicates with the third service to generate the consolidated output.

One more important aspect which you need to understand, is that the request from Service A to Service B may look different from Service B to Service C. Similarly the response from Service C to Service B may look completely different from Service B to Service A.

Asynchronous Messaging

In this type of microservices design pattern, all the services can communicate with each other, but they do not have to communicate with each other sequentially. So, if you consider 3 services: Service A, Service B, and Service C. The request from the client can be directly sent to the Service C and Service B simultaneously. These requests will be in a queue. Apart from this, the request can also be sent to Service A whose response need not have to be sent to the same service through which request has come.

Database or Shared Data

For every application, there is humongous amount of data present. So, when we break down an application from its monolithic architecture to microservices, it is very important to note that each microservice has sufficient amount of data to process a request. So, either the system can have a database per each service or it can have shared database per service.  You can use database per service and shared database per service to solve various problems.

PROBLEMS ARE:-

• Duplication of data and inconsistency
• Different services have different kinds of storage requirements
• Few business transactions can query the data, with multiple services
• De-normalization of data

Event Sourcing

The event sourcing design pattern creates events regarding the changes in the application state. Also, these events are stored as a sequence of events to help the developers track which change was made when.

So, with the help of this, you can always adjust the application state to cope up with the past changes. You can also query these events, for any data change and simultaneously publish these events from the event store. Once the events are published, you can see the changes of the application state on the presentation layer.

Branch

Branch microservice design pattern is a design pattern in which you can simultaneously process the requests and responses from  two or more independent microservices.

the request is not passed in a sequence, but the request is passed to two or more mutually exclusive microservices chains. This design pattern extends the Aggregator design pattern and provides the flexibility to produce responses from multiple chains or single chain

Command Query Responsibility Segregator

According to this pattern, the application will be divided into two parts: Command and Query. The command part will handle all the requests related to CREATE, UPDATE, DELETE while the query part will take care of the materialized views. The materialized views are updated through a sequence of events which are creating using the event source pattern 

Circuit Breaker

the Circuit Breaker design pattern is used to stop the process of request and response if a service is not working. So, for example, let’s say a client is sending a request to retrieve data from multiple services. But, due to some issues, one of the services is down. Now, there are mainly two problems you will face: 

First, since the client will not have any knowledge about a particular service being down, the request will be continuously sent to that service.

Second problem is that the network resources will be exhausted with low performance and bad user experience.

Decomposition

With the help of this pattern, either you can decompose an application based on business capability or on based on the sub-domains. For example, if you consider an e-commerce application, then you can have separate services for orders, payment, customers, products if you decompose by business capability.

The post Top 10 Checklist for Microservices design. appeared first on Artificial Intelligence.

But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

Latest released the research study on Global Cloud Microservices Market, offers a detailed overview of the factors influencing the global business scope. Cloud Microservices Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Cloud Microservices. The study covers emerging player’s data, including: competitive landscape, sales, revenue and global market share of top manufacturers are AWS (United States),CA Technologies (United States),Contino (United Kingdom),CoScale (Belgium),IBM (United States),Idexcel (United States),Infosys (India),Kontena (Finland),Macaw Software (United States),Marlabs (United States),Microsoft (United States),Netifi (United States),NGINX (United States),OpenLegacy (United States),Oracle (United States),Pivotal Software (United States).

Definition:
A cloud microservices is known as a software development technique and a collection of services which are loosely coupled. Cloud Microservices is a method of developing software systems which tries to emphasis on developing single-function modules with the well-defined operations and interfaces. It is also known as the microservice architecture. Cloud microservices are gaining popularity among companies that need greater scalability and agility. In businesses across the industries, i.e., from telecommunications and retail to the financial services and manufacturing, IT teams are choosing cloud microservices, to develop new applications and break down monoliths

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Cloud Microservices Market various segments and emerging territory.

Market Trend:
Digital transformations

Market Drivers:
Proliferation of the microservices architecture
Customer-oriented business

Restraints:
Security and compliance

The Global Cloud Microservices Market segments and Market Data Break Down are illuminated below:
by Type (Stateless, Persistence, Aggregator), Industry (BFSI, IT and ITeS, Telecommunications, Government, Healthcare, Retail and eCommerce, Media and Entertainment, Transportation and Logistics, Manufacturing, Others (Education, Energy & Utilities, and Travel & Hospitality)), Services (Consulting services, Integration services, Training, support and maintained services), Organisation (SMEs, Large Enterprises), Deployment model (Public Cloud, Private Cloud, Hybrid Cloud), Component (Platform, Services)

Analyst at AMA have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact Analysis of COVID-19 on Global Cloud Microservices Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa
Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Strategic Points Covered in Table of Content of Global Cloud Microservices Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Microservices market
Chapter 2: Exclusive Summary – the basic information of the Cloud Microservices Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud Microservices
Chapter 4: Presenting the Cloud Microservices Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying market size by Type, End User and Region 2015-2020
Chapter 6: Evaluating the leading manufacturers of the Cloud Microservices market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Cloud Microservices Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & Methodology

The primary sources involves the industry experts from the Global Cloud Microservices Market including the management organizations, processing organizations, analytics service providers of the industry’s value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources – Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Company’s Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

What benefits does AMA research study is going to provide?
• Latest industry influencing trends and development scenario
• Open up New Markets
• To Seize powerful market opportunities
• Key decision in planning and to further expand market share
• Identify Key Business Segments, Market proposition & Gap Analysis
• Assisting in allocating marketing investments

Definitively, this report will give you an unmistakable perspective on every single reality of the market without a need to allude to some other research report or an information source. Our report will give all of you the realities about the past, present, and eventual fate of the concerned Market.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Southeast Asia.

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
Unit No. 429, Parsonage Road Edison, NJ
New Jersey USA – 08837
Phone: +1 (206) 317 1218
sales@advancemarketanalytics.com

Advance Market Analytics is Global leaders of Market Research Industry provides the quantified B2B research to Fortune 500 companies on high growth emerging opportunities which will impact more than 80% of worldwide companies’ revenues.
Our Analyst is tracking high growth study with detailed statistical and in-depth analysis of market trends & dynamics that provide a complete overview of the industry. We follow an extensive research methodology coupled with critical insights related industry factors and market forces to generate the best value for our clients. We Provides reliable primary and secondary data sources, our analysts and consultants derive informative and usable data suited for our clients business needs. The research study enables clients to meet varied market objectives a from global footprint expansion to supply chain optimization and from competitor profiling to M&As.

The post Cloud Microservices Market Will Hit Big Revenues In Future | IBM, Contino, AWS appeared first on Artificial Intelligence.

We examine how monitoring and observability help development teams keep a distributed architecture from coming unraveled by individual failures and performance bottlenecks.

Failure is rarely predictable, and detecting the exact cause of complex application errors post-deployment is excruciatingly difficult. Even the most experienced development teams struggle to prepare for all the possible scenarios that could bring down their applications and put data at risk.

For this reason, the ability to detect problems in real time and address them quickly is essential. This is where observability and monitoring come into play, and architects who approach these two tasks diligently will reap the rewards of a more resilient software architecture. Let’s explore more about the specifics of observability and monitoring, including how they differ and the fundamental practices that each one dictates.

What is observability?

Observability in microservices largely revolves around making sure development teams have access to the data they need to identify problems and detect failures. For example, an observable system can help developers understand why a specific service call failed, or determine the source of bottlenecks in a particular application workflow.

With the surge in microservices adoption, it is imperative that a system is observable for effective debugging and diagnostics. Since services can span across multiple systems and run operations independently, tracing the source of a failure is a grueling and time-consuming task — if even possible.

Observability consists of three fundamental components:

• Logs are timestamped records that provide comprehensive information about an application’s behavior as it executes functions and communications. These logs are particularly useful when things go wrong in a microservices architecture, because architects can use this information to better identify specific defects and debug code.
• Metrics are numeric records of an application’s resource use, performance and stability. For example, metrics will show the number of requests a service can handle per second, or the total amount of resources an activity consumes.
• Traces keep track of IDs, names and other values and help architects monitor application transactions that cross multiple systems. This makes tracing particularly useful for microservices-based, serverless and containerized applications that rely on multitudes of integrations and asynchronous communication.

What is monitoring?

Monitoring is a process that tracks performance and identifies problems and anomalies. Overall, it describes the health, performance, efficiency and other essential features relative to the internal state.

Much like observability, monitoring can help detect and identify failures, but it does so with a focus on qualitative information. For example, you might want to monitor an application for issues such as excessive data consumption, service messaging failures or breaking changes. To use monitoring effectively, architects must determine core sets of metrics that provide a benchmark for the overall health of the system, such as acceptable latency times and call failure rates.

When monitoring microservices-based applications, architects must gain a comprehensive understanding of the various calls an application and its related services make. Don’t forget to monitor APIs and containerized services, and map monitoring processes and responsibilities based on team structure. Everyone should know who owns what service, and who needs to address a certain failure.

Microservices monitoring and observability tools

Some organizations try to adopt a manual, do-it-yourself approach to observability and monitoring by stringing homegrown monitoring solutions into their architecture. However, this takes up a lot of time, and is not likely to meet the needs of large, distributed systems.

Before attempting to do it yourself, you might want to look into existing tools designed to provide the essential aspects of monitoring and observability in microservices. Here are a few notable tools and platforms worth consideration.

Sentry

Sentry is an open source monitoring system designed with a focus on real-time, code-level error tracking that pinpoints failures and allows developers to address issues quickly. Part of Sentry’s appeal rests in its ability to analyze the scope of a failure, allowing developers to easily prioritize errors based on severity. It also features ready-made integrations with most popular development languages and frameworks, such as JavaScript, Python, Objective-C and iOS, as well as services like GitHub and Splunk.

Sensu

Sensu is another open source observability and monitoring tool that excels at monitoring applications, services, servers and containers deployed across large software ecosystems and cloud environments. Some of Sensu’s spotlight features include role-based service identification, its alignment with publish-subscribe messaging patterns and an interface that provides quick visuals of code workflows.

Sumo Logic

Thanks to this platform’s notable proficiency in data aggregation and analysis, Sumo Logic is a very useful tool for gleaning continuous metrics from application logs in real time and quickly spotting performance and stability issues in service workflows. Sumo Logic boasts a number of microservices-specific observability features, such as distributed tracing for services, transactions and application data.

The post The basics of monitoring and observability in microservices appeared first on Artificial Intelligence.

Key Takeaways

• Since 2018, several new microservices frameworks – including Micronaut, Helidon and Quarkus – have been introduced to the Java community, and have made an impact on microservices-based and cloud-native applications development.
• The MicroProfile community and specification was created to enable the more effective delivery of microservices by enterprise Java developers. This effort has influenced how developers are currently designing and building applications.
• MicroProfile will continue to evolve with changes to its current APIs and most likely the creation of new APIs.
• Developers should familiarize themselves with Heroku’s “Twelve-Factor App,” a set of guiding principles that can be applied with any language or framework in order to create cloud-ready applications.
• When it comes to the decision to build an application using either a microservices or monolithic style, developers should analyze the business requirements and technical context before choosing the tools and architectures to use.

In mid-2016, two new initiatives, MicroProfile and the Java EE Guardians (now the Jakarta EE Ambassadors), had formed as a direct response to Oracle having stagnated their efforts with the release of Java EE 8. The Java community felt that enterprise Java had fallen behind with the emergence of web services technologies for building microservices-based applications.

Introduced at Red Hat’s DevNation conference on June 27, 2016, the MicroProfile initiative was created as a collaboration of vendors – IBM, Red Hat, Tomitribe, Payara – to deliver microservices for enterprise Java. The release of MicroProfile 1.0, announced at JavaOne 2016, consisted of three JSR-based APIs considered minimal for creating microservices: JSR-346 – Contexts and Dependency Injection (CDI); JSR-353 – Java API for JSON Processing (JSON-P); and JSR-339 – Java API for RESTful Web Services (JAX-RS).

By the time MicroProfile 1.3 was released in February 2018, eight community-based APIs, complementing the original three JSR-based APIs, were created for building more robust microservices-based applications. A fourth JSR-based API, JSR-367 – Java API for JSON Binding (JSON-B), was added with the release of MicroProfile 2.0.

Originally scheduled for a June 2020 release, MicroProfile 4.0 was delayed so that the MicroProfile Working Group could be established as mandated by the Eclipse Foundation. The working group defines the MicroProfile Specification Process and a formal Steering Committee composed of organizations and Java User Groups (JUGs), namely Atlanta JUG, IBM, Jelastic, Red Hat and Tomitribe. Other organizations and JUGs are expected to join in 2021. The MicroProfile Working Group was able to release MicroProfile 4.0 on December 23, 2020 featuring updates to all 12 core APIs and alignment with Jakarta EE 8.

The founding vendors of MicroProfile offered their own microservices frameworks, namely Open Liberty (IBM), WildFly Swarm/Thorntail (Red Hat), TomEE (Tomitribe) and Payara Micro (Payara), that ultimately supported the MicroProfile initiative.

In mid-2018, Red Hat renamed WildFly Swarm, an extension of Red Hat’s core application server, WildFly, to Thorntail to provide their microservices framework with its own identity. However, less than a year later, Red Hat released Quarkus, a “Kubernetes Native Java stack tailored for OpenJDK HotSpot and GraalVM, crafted from the best-of-breed Java libraries and standards.” Dubbed “Supersonic Subatomic Java,” Quarkus quickly gained popularity in the Java community to the point that Red Hat announced Thorntail’s end-of-life in July 2020. Quarkus joined the relatively new frameworks, Micronaut and Helidon, that were introduced to the Java community less than a year earlier. With the exception of Micronaut, all of these microservices-based frameworks support the MicroProfile initiative.

The core topics for this virtual panel are threefold: first, to discuss how microservices frameworks and building cloud-native applications have been influenced by the MicroProfile initiative. Second, to explore the approaches to developing cloud-native applications with microservices and monoliths, and also the recent trend in reverting back to monolith-based application development. And third, to debate several best practices for building microservices-based and cloud-native applications.

The post Virtual Panel: The MicroProfile Influence on Microservices Frameworks appeared first on Artificial Intelligence.