motherduck-model-data

To design an analytics schema in MotherDuck without scattered SQL, use a file-based modeling scaffold that generates a structured project with raw, staging, and analytics layers. This approach produces reviewable SQL files organized by lifecycle stage instead of one-off scripts.

You build a dependency DAG for multi-stage DuckDB transformations by generating a model_manifest.yml file. This manifest captures explicit dependencies, materialization strategies, and model metadata to orchestrate reproducible rebuilds across your analytics tables.

The best way to organize ETL orchestration files for MotherDuck analytics workloads is a lightweight project structure separating raw, staging, and analytics layers. This scaffold defines relationships, explicit data types, and nullability for read-optimized modeling patterns.

You can define explicit materialization strategies for DuckDB data models by declaring them inside a model_manifest.yml. This manifest captures dependency tracking and model metadata, enabling reproducible multi-stage transformation workflows for your analytics tables.

MotherDuck supports denormalized, read-optimized schema design patterns through analytics-first modeling scaffolds. These scaffolds guide schema decisions by enforcing explicit data types, nullability constraints, and fully qualified names across your multi-stage transformation workflows.