semantic-layer-setup

To build a Databricks semantic layer, you define business metrics and dimensions in YAML Metric Views, create parameterized Table-Valued Functions, and configure Genie Spaces for natural language querying. This orchestrates end-to-end creation and validates artifacts against a project manifest.

A Databricks semantic layer defines standardized business metrics and parameterized data access functions to ensure consistency. You need Metric Views and TVFs when a data team requires discoverable, reusable logic for AI-powered Genie Spaces to answer business questions accurately.

You create parameterized SQL functions for Genie Spaces by developing Table-Valued Functions (TVFs) that enable dynamic data access. The semantic layer setup orchestrates this process, validating dependencies and bundling assets to deploy these functions idempotently.

Yes, you can deploy Databricks semantic layer components idempotently using asset bundle configurations. The setup validates components against a project manifest and manages dependencies to ensure consistent artifact generation and deployment across environments.

You define business metrics in YAML using Metric Views to establish discoverability and consistency for dimensions like Monthly Recurring Revenue. These definitions are then validated against a project manifest to generate artifacts for Genie Spaces.

The setup requires managing dependencies on foundational skills for asset bundling and naming standards. If the project manifest validation fails or prerequisites are missing, artifact generation for Metric Views, TVFs, and Genie Spaces will not proceed correctly.