Warehouse Native Experimentation allows organizations to conduct experiments directly within their data warehouses, like Snowflake and Amazon Redshift, eliminating the need for data exports to external systems. This approach provides teams with faster, more trustworthy, and transparent results because experiments are conducted using the same trusted data sources the business relies on. By integrating experimentation with internal data models and governance controls, teams can make data-driven decisions with greater confidence. This methodology supports the creation and reuse of metrics that reflect business goals, ensuring that experiments align with organizational objectives. As product velocity becomes a competitive advantage, this approach helps maintain speed and compliance, reducing risks associated with launching new features. With Warehouse Native Experimentation, teams benefit from full visibility into experiment data, allowing for comprehensive analysis, validation, and collaboration across departments, from product development to data science. Looking forward, the integration of these workflows into CI/CD pipelines aims to further streamline the experimentation process, enhancing both efficiency and confidence in product releases.