nw-data-architecture-patterns

Use a decision tree to select between data warehouse, data lake, lakehouse, and data mesh based on your organizational needs. This approach evaluates governance, scale, and analytics goals to reduce data silos and optimize query performance.

ETL transforms data before loading it into the target system, whereas ELT loads raw data first and relies on the target warehouse or lakehouse engine to perform transformations. Choosing between them depends on your platform's compute capabilities.

Use a star schema for simplified queries and faster analytics via denormalized tables, or a snowflake schema for normalized data storage and governance. Select based on whether your priority is query performance or storage efficiency.

Apply specialized scaling strategies and governance patterns to your data lakehouse to handle streaming analytics. This ensures your architecture maintains schema design integrity and query performance during real-time data ingestion.

Identify and apply appropriate data architecture patterns like lakehouse or mesh to unify analytics and reduce data silos. Selecting the right pattern streamlines data governance and optimizes query performance across your platform.