This article provides a comprehensive guide for building an end-to-end development lifecycle for AWS Glue data integration pipelines, covering planning through maintenance phases.

• Six SDLC phases: Plan, Design, Implement, Test, Deploy, and Maintain for data pipelines
• Local development and testing using Docker containers with AWS Glue ETL libraries
• Infrastructure-as-code approach using AWS CDK and CloudFormation for resource provisioning
• CI/CD automation via AWS CodePipeline, CodeCommit, and CodeBuild across environments
• Separate dev, pre-prod, and prod accounts for safe deployment and testing
• Unit testing with pytest and integration testing for AWS Glue jobs
• Baseline template (aws-glue-cdk-baseline) provided for quick implementation
• Automated deployment triggered by Git commits using CloudFormation change sets

The article demonstrates implementing a new Glue job with local testing, deploying to dev environment, running integration tests, and promoting to production with approval gates.