Creating and Consuming Metric Views

Learn how Databricks Metric Views solve the critical problem of inconsistent business metric calculations across organizations that undermine data trust and slow decision-making. Discover how to define business metrics once in a centralized, governed semantic layer and reuse them widely across your data ecosystem. Master creating and governing metric views by establishing standardized metric definitions, then explore using the AI assistant to easily customize metric views with filters, dimensions, and measures. Understand the governance workflow for publishing metric views to Unity Catalog, certifying them as trusted definitions, and applying fine-grained access control through permissions. Explore how metric views unify metrics across your entire data ecosystem, supporting data in cloud data lakes like Azure Data Lake, AWS S3, and Google Cloud Storage, as well as federated sources like Snowflake. Practice consuming metric views consistently across different platforms including SQL queries, dashboards, and Genie to ensure everyone receives the same trusted, consistent answers. Dive into drilling down into semantic models, setting up alerts based on metric views, creating dashboards that leverage these standardized metrics, and utilizing Genie's semantic understanding and continuous learning capabilities for natural language querying of your metric definitions.