Name: Anil Kumar laghuvarapu
Type: User
Company: HCL
Bio: A passionate and organized professional with 11+ years of experience in IT, Unix, Linux admin, Storage admin, Devops, Cloud & Shell Scripting.
Anil Kumar laghuvarapu's Projects
These project focus on Continuous Integration, and contain installation and configuration of Jenkins on Amazon EC2 using Hashicorp Terraform (Open Source)
Config files for my GitHub profile.
An example CloudFormation template that deploys a container to AWS Fargate as a service.
This module provisions AWS S3 buckets configured for static website hosting.
Azure Quickstart Templates
Build a CI/CD pipeline for a microservices application
Train Schedule sample app for Jenkins Pipelines exercises
Train Schedule sample app for Git exercises
1082 - Designing Applications for Kubernetes, Will Boyd
In this project, you’ll deploy web servers for a highly available web app using CloudFormation. You will write the code that creates and deploys the infrastructure and application for an Instagram-like app from the ground up
The cloud is perfect for hosting static websites that only include HTML, CSS, and JavaScript files that require no server-side processing. In this project, you will deploy a static website to AWS. First, you will create a S3 bucket and upload the website files to your bucket. Next, you will configure the bucket for website hosting and secure it using IAM policies. Next, you will speed up content delivery using AWS’ content distribution network service, CloudFront. Lastly, you will access your website in a browser using the unique CloudFront endpoint.
Build and Run a Containerized Web Application using Docker and Amazon Elastic Container Service (ECS)
Example distributed app composed of multiple containers for Docker, Compose, Swarm, and Kubernetes
Train Schedule sample app for Jenkins exercises
In this project, you will build on the skills acquired during this course. You will create and run an instance on AWS, configure Jenkins, and create a pipeline to deploy a static website on S3.
Code used in Microsoft Learn modules to support Azure DevOps