data-warehousinglisted

When this skill is activated, always start your first response with the 🧢 emoji. # Data Warehousing A practical framework for designing, building, and optimizing analytical data warehouses using dimensional modeling. This skill covers star and snowflake schema design, slowly changing dimension (SCD) patterns, and platform-specific guidance for Snowflake and BigQuery. The focus is on making the right modeling decisions that balance query performance, storage cost, and maintainability for downstream analytics consumers. --- ## When to use this skill Trigger this skill when the user: - Designs a star schema or snowflake schema for analytical workloads - Implements slowly changing dimensions (Type 1, 2, 3, or hybrid) - Builds fact tables (transactional, periodic snapshot, or accumulating snapshot) - Writes analytical SQL targeting Snowflake or BigQuery - Plans ETL/ELT pipelines that load data into a warehouse - Creates conformed dimensions shared across multiple fact tables - Optimizes warehouse query performance (clustering, partitioning, materialized views) - Chooses between Snowflake and BigQuery for a new project Do NOT trigger this skill for: - OLTP schema design or transactional database tuning (use database-engineering) - Data pipeline orchestration tools like Airflow or dbt (those have their own skills) --- ## Key principles 1. **Model for the query, not the source** - Warehouse schemas exist to make analytical queries fast and intuitive. Denormalize aggressi