jbalajiavm/boto3-aws

Repository Role

This is a learning repository for a “100 Days of DevOps” curriculum, not a single deployable application. It contains independent AWS, Terraform, Python, Bash, Kubernetes, Docker, and CI/CD examples. The root README.md is the curriculum index; most directories are standalone execution units.

Entry Points and Execution Model

There is no central runtime or orchestrator. Engineers run artifacts directly according to their technology:

• Terraform roots are applied independently, e.g. main.tf, two-tier-environment/main.tf, and aws-lambda/lambda.tf.

• Python automation scripts are invoked directly, e.g. aws-lambda/lambda.py and boto3/ec2_instance.py.

• Kubernetes resources are applied with kubectl, e.g. argocd_demo/yamls/deployment.yaml.

• Jenkins workflows are executed by Jenkins from docker_jenkins/Jenkinsfile or jenkins-terraform-pipeline/ec2_pipeline/Jenkinsfile.

AWS Infrastructure Provisioning

Terraform is the primary infrastructure-as-code boundary. Small examples provision individual AWS capabilities—S3, KMS, networking, CloudTrail, Lambda, and monitoring—while each directory owns its own state/configuration assumptions.

• Base or standalone stacks: main.tf, cloudtrail.tf, aws_config/main.tf.

• Network and storage examples: nat_gateway/main.tf, s3-crossregion-replication/main.tf, vpc_flow_logs/main.tf.

• Encryption and policy boundaries: kms/main.tf, s3_kms/main.tf, iam_policies/1-1-allow-access-to-EC2-S3.json.

Two-Tier AWS Reference Composition

two-tier-environment/ is the clearest multi-component architecture. Its root Terraform configuration composes modules for VPC networking, EC2 compute, load balancing, Auto Scaling, MySQL RDS, and SNS notifications.

• Composition root: two-tier-environment/main.tf, two-tier-environment/variables.tf.

• Network-to-compute flow: two-tier-environment/vpc_networking/main.tf supplies infrastructure consumed by two-tier-environment/ec2_instance/main.tf and two-tier-environment/auto-scaling/main.tf.

• Request/data path: Application Load Balancer → EC2/Auto Scaling workload → RDS MySQL, represented by two-tier-environment/application_load_balancer/outputs.tf, two-tier-environment/rds_mysql/main.tf, and two-tier-environment/ec2_instance/userdata.tpl.

• Notification output is isolated in two-tier-environment/sns/main.tf.

Event-Driven and Programmatic AWS Automation

Python scripts use AWS SDK/Boto3 and Lambda handlers to operate AWS resources at runtime. IAM trust and permission documents form the security boundary between function execution and EC2 operations.

• Lambda deployment/handler examples: aws-lambda/lambda.py, aws-lambda/ec2_stop_start_instances_updated/lambda.py, aws-lambda/ec2_stop_start_instances_updated/main.tf.

• EC2 lifecycle and inventory scripts: boto3/ec2_instance.py, boto3/ec2_information/retrieve_information_about_ec2.py.

• Cleanup automation targets AMIs, EBS volumes, and snapshots: boto3/cleanup_old_ami/cleanup_old_ami.py, boto3/cleaning_old_ebs_vol/cleaning_old_ebs_vol.py, boto3/snapshot_cleanup/snapshot_cleanup.py.

• External service boundary: AWS APIs for EC2, S3, IAM, Lambda, CloudWatch, SNS, and related services; credentials are expected from the local/AWS execution environment.

Delivery and Container Platforms

The repository demonstrates multiple separate delivery paths rather than one standardized pipeline.

• Jenkins can build/deploy containerized workloads or run Terraform: docker_jenkins/Dockerfile, docker_jenkins/Jenkinsfile, jenkins-terraform-pipeline/ec2_pipeline/main.tf.

• An EKS-oriented example packages a simple static app into a container and deploys it from manifests: eks-codepipeline/Dockerfile, eks-codepipeline/buildspec.yml, eks-codepipeline/manifests/deployment.yaml.

• Kubernetes/cluster configuration examples are independent: argocd_demo/yamls/service.yaml, kind/multi-node-master.yaml, metallb/cm.yaml.

Operational Utilities and Observability

Operational scripts provide local or AWS-facing diagnostics rather than a shared monitoring service. Log parsing, host-performance checks, Kubernetes debugging, and notification examples are standalone tools.

• Apache log parsing: apache_log_parser.py, apache_log_parsing/apache_log_parsing_v4.py.

• Host and service diagnostics: performance_tuning/performance-monitoring.py, python_script_to_check_website_status/script_to_check_website_status.py.

• Kubernetes failure investigation: kubernetes_debugging/kubernetes_debugging_select.sh, kubernetes_debugging/crash_loopback_error.sh.

• AWS alerting infrastructure: monitoring_and_alerting/sns_alert/main.tf, s3eventstosns.tf.