Constructs standardized dbt models following Kimball dimensional patterns and modern testing configurations.
Streamlines the creation of robust dbt models by automating the development of staging layers, fact tables, dimension tables, and analytical marts. It enforces strict data engineering best practices, including per-model YAML documentation, External Parquet partitioning for large datasets, and the latest dbt 1.10+ test syntax. By guiding users through complex workflows like Slowly Changing Dimensions (SCD Type 2) and the 2×2 stat model quadrant, it ensures that analytics repositories remain modular, highly performant, and consistently documented.
Key Features
01External Parquet table optimization with custom partitioning logic
02SCD Type 2 pattern support for historical data tracking
03Advanced testing configurations using dbt 1.10+ data_tests syntax
04Standardized templates for staging, facts, dimensions, and analytical marts
050 GitHub stars
06Implementation of Kimball dimensional modeling and 2x2 stat architectures
Use Cases
01Designing a new analytics-ready data layer from raw source data
02Updating existing dbt projects to comply with modern testing and documentation standards
03Converting complex statistical event logs into structured fact and dimension tables
