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semantic-layer-setup

Orchestrate Databricks semantic layer creation with Metric Views, TVFs, and Genie Spaces.

1|Updated Jan 22, 2026
Search Tags:#orchestration#semantic layer#databricks#genie#data product#metric views#tvf
One-click install
npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill semantic-layer-setup-prashsub
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: semantic-layer-setup
Source: https://github.com/prashsub/vibe_coding_lakehouse_starter_repo/tree/main/data_product_accelerator/skills/semantic-layer/00-semantic-layer-setup
Command: npx skills add https://github.com/prashsub/vibe_coding_lakehouse_starter_repo --skill semantic-layer-setup-prashsub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

This Skill automates the creation of a robust Databricks semantic layer, including Metric Views, Table-Valued Functions, and Genie Spaces, directly from your Gold layer data.

Core Features & Use Cases

  • End-to-End Orchestration: Guides users through the entire process of building the semantic layer.
  • Metric View Creation: Defines semantic definitions for Gold tables, enabling business-friendly data access.
  • TVF Development: Creates parameterized SQL functions for use in BI tools and AI agents.
  • Genie Space Setup: Configures Genie Spaces with agent instructions and data assets for natural language querying.
  • Use Case: When establishing a new data product, use this Skill to orchestrate the creation of all necessary semantic layer components, ensuring consistency and adherence to project plans.

Quick Start

Use the semantic-layer-setup skill to build the Databricks semantic layer end-to-end, creating Metric Views and TVFs for Genie, or setting up Genie Spaces.

Frequently Asked Questions about semantic-layer-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a Databricks semantic layer end-to-end from Gold layer data?▼

Building a Databricks semantic layer requires orchestrating Metric Views, Table-Valued Functions, and Genie Spaces from Gold layer data. This end-to-end process establishes semantic definitions enabling business-friendly data access and natural language querying.

What are Metric Views and Table-Valued Functions used for in Databricks Genie?▼

Metric Views define semantic definitions for Gold tables, while Table-Valued Functions provide parameterized SQL for BI tools and AI agents. Both components are required to set up Databricks Genie Spaces for natural language querying.

How do I set up Genie Spaces with data assets and agent instructions?▼

Setting up Genie Spaces involves configuring data assets like Metric Views and TVFs alongside specific agent instructions. This enables the Databricks Genie feature to process natural language queries against your semantic layer accurately.

Can I automate Databricks Genie Space deployment using an API?▼

Yes, API-driven deployment automates Genie Space creation as part of an end-to-end Databricks semantic layer setup. This ensures consistent configuration of data assets and agent instructions across new data products.

Do I need Gold layer tables before creating a semantic layer in Databricks?▼

Yes, Gold layer tables are required as the foundational data source for creating a Databricks semantic layer. The orchestration process uses these refined tables to define Metric Views and Table-Valued Functions for your data product.

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