pipeline-design

To build a standardized Python ETL pipeline, use a framework-agnostic architecture implementing specific directory layouts, file state machines, and config-first dataclasses. This ensures modular, auditable, and idempotent workflows across orchestrators like Airflow or Prefect.

A standardized file state machine for ETL pipelines enforces a file-handling lifecycle across ingest, process, store, and quarantine stages. This mechanism ensures data is never lost and remains fully auditable throughout workflow execution.

Perform idempotent bulk upserts in SQLAlchemy for an ETL pipeline by using schema-aware ORM modeling helpers and specific database interaction patterns. This ensures database integrity and allows safe retries without duplicating data.

Yes, you can use this pipeline design approach with both Airflow and Prefect. It provides a framework-agnostic architecture to build repeatable, production-grade data pipelines that can be easily monitored, retried, and scaled across various orchestrators.

The best way to reduce boilerplate configuration in Python data engineering pipelines is using a config-first design. By defining pipeline behavior through a single dataclass, you centralize configuration and eliminate repetitive setup code.