Creating and Consuming Metric Views

In this video, you will learn how Databricks Metric View solves the problem of having the same business metrics calculated in different ways across an organization, which can kill trust in data and slow down decision-making. The solution allows you to define business metrics once and reuse them widely. 🎯 What You'll Learn: How to create and govern metric views by defining metrics once in a centralized, governed semantic layer How to use the AI assistant to easily customize a metric view How to publish the metric view to Unity Catalog, certify it as a governed, trusted definition, and apply fine-grained access control using permissions How metric views unify metrics across your entire data ecosystem, supporting data in cloud data lakes (Azure Data Lake, AWS S3, Google Cloud Storage) and federated sources like Snowflake How to consume metric views consistently in SQL queries, dashboards, and Genie, ensuring everyone gets the same trusted, consistent answer ⏱️ CHAPTERS: 01:02 – Creating a Metric View and Semantic Layer 01:33 – Customizing with Filters, Dimensions, and Measures (Using AI Assistant) 03:09 – Governance and Publishing to Unity Catalog 03:27 – Permissions, Lineage and Insights 04:14 – Connectivity and Cross-Platform Unification 04:49 – Consuming Metric Views in SQL Queries 06:12 – Drilling into the Semantic Model 07:39 – Consuming Metric Views with Alerts 08:41 – Consuming Metric Views with Dashboards 09:34 – Consuming Metric Views with Genie 10:00 – Genie's Semantic Understanding and Continuous Learning 📚 Resources: Databricks Documentation on Metric Views: https://docs.databricks.com/aws/en/me... 🤝Stay connected: Connect with us: Website: https://databricks.com Twitter: https://x.com/databricks/ LinkedIn:   / databricks   Instagram:   / databricksinc   Facebook:   / databricksinc