FlowerPower Pipeline Framework

🌸 Build configuration-driven data pipelines using Hamilton DAGs. Lightweight, modular, and perfect for batch ETL, data transformation, and ML workflows.

FlowerPower is ideal for:

• Simple to medium complexity data pipelines (not full production orchestration)
• Teams wanting code-first DAG definitions (vs. YAML-heavy Airflow)
• Projects needing configurable parameters and multiple executors
• Rapid prototyping and batch processing

For production orchestration with scheduling, state persistence, and reliability features, see @data-engineering-orchestration (Prefect, Dagster, dbt).

Skill Dependencies