Snowflake Data Warehouse Architecture: How to Organize Databases, Schemas and Tables

The article discusses organizing a Snowflake data warehouse architecture, focusing on databases, schemas, tables, and views. It recommends creating separate databases for raw data ingestion, base model transformations, complex model development and production, and reporting and experimentation. Each database should have its own set of schemas to further categorize the data within them. The article also emphasizes using appropriate naming conventions for tables and views based on their purpose in the data pipeline. It suggests that this architecture can be customized according to specific business use cases and requirements, and may need adjustments over time as new needs arise.