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🚀 Supercharge Your CI/CD Pipeline: GitHub Actions with Kubernetes Deployment

GitHub Actions, a powerful automation tool integrated right into your GitHub repository, can be combined with Kubernetes to streamline your deployment process.
Combining GitHub Actions and Kubernetes provides a seamless pipeline from code commit to deployment, ensuring that your application is always up-to-date and running smoothly.
Before we dive in, make sure you have the following: GitHub Account: Where your repository will live. Kubernetes Cluster: You can use Minikube for local development or a cloud provider like GKE, EKS, or AKS. Docker Installed: For building container images.
Benefits of GitHub Actions: Integration: Directly integrated with GitHub repositories, Customization: Write workflows using YAML syntax, Community: Vast marketplace of pre-built actions.
Benefits of Kubernetes: Scalability: Automatically scale applications based on load, Self-Healing: Restarts containers that fail, Rollouts and Rollbacks: Deploy new versions safely.
Some of the prerequisites are GitHub Account, Kubernetes Cluster, Docker Installed, and Basic Understanding of YAML.
By leveraging GitHub Actions and Kubernetes, we've automated our CI/CD pipeline, reducing manual intervention and the risk of errors.
Useful Tips - Use Specific Tags: Instead of latest, tag your images with commit SHAs or version numbers, Namespace Isolation: Deploy to specific namespaces to avoid clashing with other services.
Certainly! Here are the two lines you can include in your article to reference your GitHub repository: 🔗 GitHub Repository You can find all the code and configuration files for this project in my GitHub repository: github-actions-kubernetes. Feel free to clone or fork the repo to set up your own CI/CD pipeline!
Benefits of Slack Notifications - Get notified with a funny message when your deployment succeeds or fails. Enable Incoming Webhooks. Create a webhook URL.

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Medium

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AI Development Stack with Cloud: Empowering the Future of AI

Cloud platforms offer scalable and cost-effective resources for AI applications.
NVIDIA's Kubernetes-powered cloud infrastructure enables developers to deploy AI applications on serverless containers equipped with GPUs and TPUs.
Microservices, Docker containers, and tools like Apache Kafka are used for building and deploying AI systems.
The AI Development Stack provides a structured approach from prototyping to advanced AI solutions, integrating cloud technologies and advanced tools.

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Dev

314

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Resolving ECS Task Definition Security Risks Detected by AWS Security Hub Using Secrets Manager

AWS Security Hub detected a security risk related to ECS task definitions: storing database passwords in plaintext in the task definition environment variables.
The issue was addressed by transitioning to AWS Secrets Manager to securely manage database passwords.
The solution eliminates the risk of storing plaintext secrets and aligns with AWS's best practices for managing sensitive information.
Using Secrets Manager improves security, maintainability, and allows for dynamic retrieval of passwords in ECS task definitions.

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Dev

247

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React Router: React 18 to 19 with Style and Simplicity

React Router is a powerful and versatile solution for handling navigation in React applications.
It offers backward compatibility, future-forward features, and can be used as a library or a framework.
In its minimal form, React Router works as a simple, declarative routing library for SPAs.
For more complex projects, React Router can be used as a full-fledged framework with features like hot module replacement, code splitting, and server rendering.

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Dev

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Building a Secure and Scalable CI/CD Pipeline for EKS Using Jenkins and GitHub Actions

Building a secure CI/CD pipeline is required to deploy apps to multiple environments. This blog explains the necessary CI/CD pipeline implementation using Jenkins and Github Actions with various stages of deployment.
The pipeline stages include Source Code Management, Build and Test, Containerization, Static Analysis and Security Scans, Continuous deployment to EKS, Monitoring, and Rollback.
Environment segregation, Branching strategy, Security and Compliance, Automated testing, and Observability are some of the best practices for CI/CD implementation.
The workflow files were used from the Github Actions project for Continuous Integration, and the Jenkins pipeline was used for Continuous Deployment.
For promoting from Dev → Test → Pre-Prod → Prod, Jenkins pipelines were used with approval gates.
The sample provided Python Flask application used for testing purposes in the article for CI/CD pipeline deployment.
Implementing scanning, automated tests, and monitoring guarantees a reliable and robust deployment process.
Overall, Jenkins and Github Actions are an excellent CI/CD orchestration tool for teams that can help implement a CI/CD pipeline to deploy applications to Amazon EKS.

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Dev

234

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Resolving the "Request Entity Too Large" Error in Helm Deployments: Effective Strategies and Solutions

Resolving the "Request Entity Too Large" Error in Helm Deployments: Effective Strategies and Solutions
This error occurs when the size of the Helm chart or the generated Kubernetes resources exceeds the default size limit set by the Kubernetes API server or ingress controllers.
Solution: Splitting an Umbrella Chart
Best Practices: Keep Charts Modular, Use External Storage, and Test Chart Output

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Dev

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How To Setup Password Hash Synchronization In Microsoft Azure

This article explains how to set up password hash synchronization in Microsoft Azure.
Sync users and groups from the on-premise directory user AD.
Download and run the Azure AD connect setup file.
Configure Azure AD connect software and verify synchronization.

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Dev

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Canary Deployments: A Safer Way to Roll Out Updates

Canary deployment is a strategy where a new software version is released to a small subset of users before a full rollout.
The deployment involves running two versions of the application simultaneously.
Developers monitor user interactions, system performance, and error logs to assess the new version's impact.
This approach reduces the risk of widespread issues, allows for real-time feedback, and facilitates a smoother transition to new software versions.

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Dev

101

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Crafting a Multi-Environment CI/CD Pipeline: A Hands-on Guide with Tekton and ArgoCD

Setting up a multi-environment CI/CD pipeline using Tekton and ArgoCD
Building a complete CI/CD pipeline with automated code build and test, multiple environment deployment, secure handling of secrets, monitoring, and rollback capabilities
Installation and setup of Tekton, including fixing workspace mounting problems
Integration of ArgoCD for handling deployments, including resolving issues with automatic sync and configuration management

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Dev

428

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k9s - manage your Kubernetes cluster and it’s objects like a pro!

k9s is a terminal based GUI to manage any Kubernetes(k8s) cluster.
Features include showing a helicopter view of all Kubernetes objects, moving between namespaces, observing object states through pulses, managing objects directly from CLI, accessing shell of pods, viewing logs, sanitizing pods, etc.
Installation is straightforward and available for various platforms.
k9s offers easy navigation between Kubernetes objects, pod management including deletion and shell access, describing pod details, accessing YAML output, viewing logs, and doing port-forwarding.

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Dev

234

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How to Protect ECS Containers with a Read-Only Root Filesystem

Enable ReadonlyRootFilesystem to enforce a read-only root filesystem in ECS containers.
Handle temporary file requirements by defining a volume and mounting it to /tmp directory.
Consider redirecting application write operations to /tmp or another designated writable directory.
Standardize logging by directing logs to stdout and integrating with logging services.

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Dev

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Steps to install SQLITE 3 on windows and and create a table.

SQL is a standard programming language used to manage databases.
SQLite 3 is a lightweight, self-contained database management system.
To install SQLite 3, download the tools from the official website and extract the files.
To create a table, use the CREATE TABLE command with column names and data types.

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Dev

309

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🐘 Simplified Guide to Setting Up PostgreSQL on macOS with Docker 🚢

Struggling with PostgreSQL configuration on macOS? This guide provides clear step-by-step instructions for setting up PostgreSQL on macOS using Homebrew and Docker. It addresses the limited client tools and sparse documentation specific to macOS users.
Step 1: Install PostgreSQL using Homebrew. Start the PostgreSQL service and verify the service status.
Step 2: Create a PostgreSQL user and database. Access the PostgreSQL interactive shell, create a user and database, and grant access.
Step 3: Connect to your database. Test the connection using the provided command.

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Dev

300

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Top 12 DevSecOps Tools for 2025

78% of enterprises plan to integrate AI into their development by 2025 and the pressure to secure these workflows is greater than ever.
DevSecOps tools integrate security into the entire DevOps process with aims to catch vulnerabilities early to reduce the likelihood of post-deployment issues.
DevSecOps tools cover a lot of ground today, including supply chain security, static application security testing, dynamic and interactive application security testing, container security, infrastructure as code security, and software composition analysis.
The benefits of DevSecOps tools include built-in security throughout development, instant threat detection, smart risk prioritization, faster releases, and automated fixes.
Some of the top DevSecOps tools for 2025 include Myrror, Jit, Semgrep, SonarQube Server, OWASP ZAP, and Veracode for SAST; Trivy, Anchore for container security; Checkov and KICS for IaC security; and Spectral and Synk for software composition analysis.
DevSecOps tools allow security to keep up with the fast development pace of today and are essential for companies whose software directly drives revenue.

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Dev

389

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End-to-End AWS DevOps Project: Automating Build and Deployment of a Node.js Application to Amazon ECS using GitLab CI/CD

This article explains the process of automating the build and deployment of a node.js application to Amazon ECS using GitLab CI/CD.
The project makes use of GitLab as version control, Docker for containerization and AWS services like ECS, ECR, etc.
The article lists the technology stack used, architecture diagram followed for better understanding of the process.
The article also provides detailed steps for prerequisites, configuring Gitlab as version control and preparing AWS resources.
The article highlights the process of building and pushing Docker image, setting up Amazon ECS with Fargate and creating GitLab CI/CD pipeline.
CloudWatch is used for enabling monitoring and logging, and creating alarms for monitoring metrics such as CPU usage, memory and application errors.
The article provides a detailed explanation of every step with relevant commands that make it easy to implement.
By the end of this guide, you will have a complete understanding of the CI/CD workflow in AWS.
This article is a must-read for DevOps engineers who want to learn how to automate the build and deployment processes of their Node.js applications to Amazon ECS efficiently.
The author also shares the GitHub profile for more DevOps related content.

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