data-architecture-spec1

What problem does it solve?

This Skill eliminates the complexity and potential for errors in designing, validating, and integrating data pipelines. It ensures adherence to robust architectural patterns like SPEC-1, standardizing schema design, data modeling, identity resolution, and data quality frameworks. This saves time, reduces rework, and builds trust in your data platform.

Core Features & Use Cases

• Standardized Pipeline Design: Integrate new data sources (e.g., nflverse, KTC, Commissioner Sheets) following established batch ingestion patterns and storage layouts.
• Kimball Dimensional Modeling: Design and validate dbt models (staging, facts, dimensions, marts) for 2x2 stat models (actuals vs. projections, real-world vs. fantasy) and slowly changing dimensions.
• Robust Data Quality & Identity Resolution: Implement comprehensive data quality checks (grain, FK, enums) and canonical player ID mapping across 19 providers.
• Use Case: You need to integrate a new data source for player projections. This Skill guides you through defining the ingestion pattern, mapping player IDs to the canonical mfl_id, creating staging and fact tables that comply with the 2x2 model, and setting up all necessary dbt tests to ensure data quality.

Quick Start

Help me design a new data source integration for KTC market values, ensuring it follows SPEC-1 patterns for batch ingestion, player identity resolution, and data quality testing.