convention-data-validation

What problem does it solve?

Data validation is essential to ensure input integrity, catch errors early, and maintain trustworthy analytics across pipelines.

Core Features & Use Cases

• Pydantic-based input validation for API and function parameters
• Great Expectations-driven dataset validation and quality checks
• Schema validation and data quality metrics to surface issues early
• Use Case: validating ETL inputs and ensuring downstream consistency in data pipelines

Quick Start

Run a validation pipeline that loads raw data, applies Pydantic models, and executes a GE-based validation suite to produce a quality report.