flowerpower
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
This skill assumes familiarity with:
@data-engineering-core- Polars, DuckDB, PyArrow basics@data-engineering-storage-lakehouse- Delta Lake, Iceberg table formats
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