These projects cover various aspects of DevOps practices and technologies, integrating AWS services and other DevOps tools to solve real-world problems.
Project 1: Automated Deployment Pipeline with AWS CodePipeline: a. Problem Statement: Develop an automated deployment pipeline using AWS CodePipeline to deploy a Python application stored in AWS CodeCommit. b. Configure CodeBuild for building the application and deploying it to AWS Elastic Beanstalk or AWS Lambda.
Project 2: Infrastructure as Code with Terraform and AWS: a. Problem Statement: Create infrastructure as code (IaC) scripts using Terraform to provision AWS resources like EC2 instances, S3 buckets, and RDS databases. b. Write Python scripts to automate the Terraform workflow, including initialization, planning, applying changes, and destroying resources.
Project 3: Continuous Monitoring and Alerting with AWS CloudWatch: a. Problem Statement: Set up continuous monitoring and alerting for a Python application deployed on AWS using AWS CloudWatch. b. Configure custom metrics, alarms, and notifications to monitor application performance and health.
Project 4: Container Orchestration with AWS ECS or EKS: a. Problem Statement: Implement container orchestration using AWS Elastic Container Service (ECS) or Elastic Kubernetes Service (EKS) to deploy and manage Docker containers running Python applications. b. Develop automation scripts to deploy, scale, and manage containerized applications.
Project 5: Serverless Data Processing with AWS Lambda and Python: a. Problem Statement: Design and implement a serverless data processing pipeline using AWS Lambda and Python. b. Utilize AWS services like S3, DynamoDB, and SQS for storing data, triggering Lambda functions, and processing data asynchronously.
Project 6: Continuous Integration with Jenkins and AWS: a. Problem Statement: Set up a continuous integration (CI) pipeline using Jenkins to automate the build, test, and deployment of Python applications on AWS. b. Integrate Jenkins with AWS services like S3, EC2, and Lambda to perform various stages of the CI/CD process.
Project 7: Configuration Management with Ansible and AWS: a. Problem Statement: Manage the configuration of AWS resources using Ansible playbooks written in Python. b. Define infrastructure configurations, deploy applications, and automate routine tasks such as system updates, backups, and security configurations.
Project 8: Infrastructure Monitoring and Visualization with Grafana: a. Problem Statement: Configure Grafana to visualize metrics and logs collected from AWS CloudWatch, Prometheus, or other monitoring tools. b. Develop Python scripts to collect and send custom metrics to Grafana for monitoring infrastructure and application performance.
Project 9: Security Automation with AWS Security Hub and Python Boto3: a. Problem Statement: Implement security automation using AWS Security Hub and Python Boto3 library. b. Write Python scripts to automate security compliance checks, remediation actions, and incident response workflows based on predefined security policies.
Project 10: High Availability and Disaster Recovery Architecture on AWS: a. Problem Statement: Design and implement a high availability (HA) and disaster recovery (DR) architecture for a Python application deployed on AWS. b. Utilize AWS services such as Auto Scaling, Elastic Load Balancing, Route 53, and Multi-AZ deployments to ensure application availability and data durability.