Data Pipeline Orchestration (Python, Airflow, dbt, PostgreSQL, Docker) overview

A data pipeline orchestration platform built around Apache Airflow for workflow scheduling and dbt for SQL-based data transformations, creating a modern ELT stack where raw data lands in PostgreSQL and is progressively refined through dbt models into analytics-ready tables. Airflow DAGs coordinate extraction from source systems, dbt model runs, data quality checks, and downstream notifications. Python scripts handle custom extraction logic and API integrations. SQLAlchemy provides programmatic database access for pipeline metadata. Docker Compose runs the complete Airflow cluster (scheduler, webserver, workers) alongside PostgreSQL for local development. The tradeoff is Airflow's operational complexity and the learning curve of dbt's ref-based dependency graph, but the combination provides unmatched visibility into data lineage.