This project aims to create a platform with different tools and approaches to provision secure and reliable infrastructure on the cloud.

• terraform-actions/ - Run locally to Configure the trust between GitHub Actions and AWS account.
• terraform-infra/ - Deploys infrastructure on AWS cloud. It will become the base for infra templates.

• Setup S3 and DynamoDB to manage tfstate
• Refer to current branch, commit.
• Write .md files for each terraform-<xxx> to explain approaches and choices, share hints.
• Setup environments (develop and production)

• Cloud: AWS
• CI/CD: Github Actions
• IaC: Terraform

• AWS Account: AWS Free Tier
• IAM User: Create an IAM user with programmatic access and adequate permissions for managing resources.
• Network: VPC, subnets etc.
• Storage
• EKS cluster

• Install Terraform: Download and install Terraform on local machine.
• Terraform Configurations: Modular setup where each directory focused on exact function (terraform-<function>/).

• Ansible Roles
• Bash scripts
• Python

• Github Actions, define workflow, jobs, steps. Explore and improve
• Continuous Integration: Code build, unit tests, and integration tests.
• Continuous Deployment: Automated deployment of the application to the Kubernetes cluster upon successful build and tests.

• Docker: Containerize application by writing a Dockerfile and building a Docker image.
• Kubernetes Deployment: Write Kubernetes deployment and service YAML files to define how application should be deployed and exposed within the cluster.

• ELK Deployment: Deploy Elasticsearch, Logstash, and Kibana either on EC2 instances or within Kubernetes (as separate pods or using a managed service like Amazon Elasticsearch Service).
• Log Collection: Configure Logstash or Filebeat to collect logs from application and other services, and send them to Elasticsearch.
• Dashboards: Use Kibana to create dashboards for visualizing logs and monitoring application performance.

• Testing: Test the entire workflow from code commit, CI/CD pipeline, to deployment in Kubernetes, and monitoring.
• Iteration: Refine configurations, scripts, and pipelines based on test outcomes.
• Documentation: Document setup, configurations, and any important findings or decisions made during the project.