preferences-data-modeling

Design robust data schemas by applying conventions for database normalization, ER diagrams, and type safety. This approach ensures data integrity and efficient processing across your analytics pipelines.

Manage incremental processing in data pipelines by implementing patterns for time-partitioned data and state tracking. This allows efficient handling of evolving datasets without reprocessing the entire pipeline.

Separate logical metrics from physical storage in a semantic layer by defining reusable business metrics independently of the underlying database schema. This ensures consistent analytics and prevents metric duplication.

Yes, this data modeling approach supports DuckDB and SQLMesh integration. It provides specific patterns for lakehouse architectures, SQL models, and pipeline orchestration within these environments.

Use data quality checks in SQL models continuously to enforce data integrity and consistency. Applying these checks during pipeline orchestration prevents invalid data from reaching your analytics layer.