Describe how you would automate the deployment of an ETL pipeline built with PySpark to AWS using GitHub Actions and AWS services. Include specifics on the tools and configurations you would use to ensure seamless deployment in testing and monitoring.

To automate a PySpark ETL pipeline on AWS, I would build a GitHub Actions workflow that triggers on pushes to the main branch. The workflow would consist of three jobs: build, test, and deploy. In the build job, I would use Docker to create a container image that contains the PySpark application and its dependencies, then push the image to Amazon ECR. The test job would run unit tests and integration tests against a local Spark cluster or a temporary EMR cluster spun up via the AWS CLI. For deployment, I would use AWS CloudFormation or Terraform to provision an EMR cluster (or an ECS Fargate task if the job is short‑lived) and then run the PySpark job using the spark-submit command. IAM roles would grant the necessary permissions to read from S3, write logs to CloudWatch, and manage EMR resources. Monitoring would be handled by CloudWatch Alarms on job metrics (e.g., duration, error count) and by sending alerts to SNS or PagerDuty. Continuous integration would also include a static code analysis step (e.g., Bandit, Flake8) and a security scan of the Docker image using Amazon Inspector. This pipeline ensures that every change is automatically built, tested, and deployed with observability and rollback capabilities.