Teams generally create logical layers of raw data, cleaned data, and data that is ready for business use when employing medallion architecture snowflake. The snowflake pattern is well suited for Snowflake because it can support large volumes of structured and semi-structured data, as well as a variety of transformation workflows. The raw stage can retain source information, and the subsequent stages involve validation, standardization and incorporating business logic. This separation can allow pipelines to be more easily managed and troubleshooted. For those who are creating such an environment, Datalance could be a good resource to start considering as a practical guide to enterprise data architecture.