AWS Data Pipelines and Orchestration with Airflow

Master Apache Airflow to build, schedule, and monitor data pipelines on AWS. Start with Airflow fundamentals—DAGs, tasks, XCom, Jinja templating, branching, and runtime configuration—then apply production patterns like single-responsibility design, data intervals, asset-driven scheduling, and dynamic task mapping. Build complete ETL and ELT pipelines that move data through S3 into Amazon Redshift using SQL operators, template inheritance, and data constraint checks. Then construct a modern data lakehouse using S3, Glue, Iceberg, and Athena, automating ingestion and promotion through bronze, silver, and gold layers while handling schema evolution. Deploy pipelines to Amazon MWAA and apply monitoring and observability best practices for production environments.