How Sigma’s Data Platform Team Manages Snowflake Semantic Views

Over the past year, Sigma's Data Platform Team has integrated Snowflake Semantic Views into their analytics processes to enhance their semantic layer, allowing for streamlined metric definitions and improved data model interoperability with Sigma. By using dbt to manage these definitions, the team can leverage version control, peer review, and CI checks, ensuring consistency and traceability across their data models. Two main integration patterns are employed to incorporate these semantic views into Sigma: native integration for direct use of Snowflake Semantic Views as Sigma data models, and a more flexible API path for extending data models with Sigma-specific functionalities. Collaboration with non-technical stakeholders is facilitated through Sigma's platform, enabling them to interact with data models, provide feedback, and drive the evolution of the semantic layer. This structured approach has reduced ad-hoc modeling work, centralized metric definitions, and allowed various departments to access a consistent understanding of their data, ultimately promoting a cohesive and scalable data strategy.