This skill equips Claude with specialized expertise in designing and maintaining enterprise-level data warehouses using Snowflake, BigQuery, and Redshift. It focuses on implementing Kimball dimensional modeling, star schemas, and Slowly Changing Dimensions (SCD Type 2) to ensure data integrity and historical accuracy. Beyond architecture, it provides specific implementation patterns for performance tuning, such as clustering, partitioning, and materialized views, helping teams build scalable, cost-effective, and high-performance data infrastructure.
주요 기능
01SCD Type 2 implementation for historical data tracking
02Automated data infrastructure and audit column setup
03Kimball Dimensional Modeling and Star Schema design
04Performance tuning via clustering and partitioning
052 GitHub stars
06Platform-specific optimization for Snowflake and BigQuery
