Databricks Genie

Databricks Genie

Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration.

Overview

Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally.

When to Use This Skill

Use this skill when:

• Creating a new Genie Space for data exploration
• Adding sample questions to guide users
• Connecting Unity Catalog tables to a conversational interface
• Asking questions to a Genie Space programmatically (Conversation API)

MCP Tools

Space Management

Conversation API

Supporting Tools

Quick Start

1. Inspect Your Tables

Before creating a Genie Space, understand your data:

get_table_details(
    catalog="my_catalog",
    schema="sales",
    table_stat_level="SIMPLE"
)

2. Create the Genie Space

create_or_update_genie(
    display_name="Sales Analytics",
    table_identifiers=[
        "my_catalog.sales.customers",
        "my_catalog.sales.orders"
    ],
    description="Explore sales data with natural language",
    sample_questions=[
        "What were total sales last month?",
        "Who are our top 10 customers?"
    ]
)

3. Ask Questions (Conversation API)

ask_genie(
    space_id="your_space_id",
    question="What were total sales last month?"
)
# Returns: SQL, columns, data, row_count

Workflow

1. Inspect tables    → get_table_details
2. Create space      → create_or_update_genie
3. Query space       → ask_genie (or test in Databricks UI)
4. Curate (optional) → Use Databricks UI to add instructions

Reference Files

• spaces.md - Creating and managing Genie Spaces
• conversation.md - Asking questions via the Conversation API

Prerequisites

Before creating a Genie Space:

1. Tables in Unity Catalog - Bronze/silver/gold tables with the data
2. SQL Warehouse - A warehouse to execute queries (auto-detected if not specified)

Creating Tables

Use these skills in sequence:

1. synthetic-data-generation - Generate raw parquet files
2. spark-declarative-pipelines - Create bronze/silver/gold tables

Common Issues