Dimensional Modeling Skill

What problem does it solve?

This skill guides Claude in dimensional modeling using the myPub knowledge base to design scalable analytic data structures and support robust decision-making.

Core Features & Use Cases

• Dimensional modeling fundamentals: star vs snowflake, grain definition, and fact/dimension table design.
• Handling slowly changing dimensions (SCDs) including Type 1, 2, and 3.
• Conformed dimensions and bridge tables for integrating multiple data sources.
• Use Case: Build a sales analytics model with a grain of one row per sale, a fact_sales table, and dimension tables for customers, products, and time.
• Use Case: Compare Kimball, Inmon, and Data Vault approaches to choose an archival and reporting architecture.

Quick Start

Define the grain for a sample domain, identify the fact and dimension tables, and generate a starter star schema with example attributes and simple ETL notes.