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databricks-isv-connector-structure

Define a blueprint for Databricks REST or Python SDK connectors with authentication and telemetry.

5|1|Updated Apr 8, 2026
Search Tags:#authentication#telemetry#connector#api-design#databricks#isv
One-click install
npx skills add https://github.com/databricks-solutions/partner-ai-dev-kit --skill databricks-isv-connector-structure
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-isv-connector-structure
Source: https://github.com/databricks-solutions/partner-ai-dev-kit/tree/main/skills/connector-structure
Command: npx skills add https://github.com/databricks-solutions/partner-ai-dev-kit --skill databricks-isv-connector-structure

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guide provides a standardized blueprint for designing Databricks connectors (REST-based or Python SDK-based), ensuring consistent authentication, telemetry, and operation structure across projects.

Core Features & Use Cases

  • Defines a single, consistent config shape and a clear integration path for REST or Python SDK connectors.
  • Recommends supporting PAT, OAuth M2M, and OAuth U2M authentication with per-connection isolation and a centralized User-Agent strategy.
  • Provides a clear connect(config) entry point, token handling, and validation guidance to ensure reliable deployments.

Quick Start

Follow this guide to implement the connector structure in your project and begin wiring up connect(config) with the recommended auth and validation patterns.

Frequently Asked Questions about databricks-isv-connector-structure

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

FAQPage Schema
How do I structure a Databricks connector for consistent authentication?▼

A Databricks connector should use a single connect(config) entry point with per-connection auth isolation and centralized User-Agent handling. This blueprint standardizes authentication across REST and Python SDK integrations.

What authentication methods should a Databricks connector support?▼

Databricks connectors should support PAT, OAuth M2M, and OAuth U2M authentication flows. This blueprint provides token handling and validation guidance to ensure reliable deployments across REST and Python SDK integrations.

Can I use the Python SDK with this Databricks connector blueprint?▼

Yes, this connector blueprint defines a consistent integration path for both REST-based and Python SDK-based connectors. It recommends a standardized config shape to ensure correct operations across either integration method.

What is the best way to handle token refresh in Databricks connectors?▼

The best way to handle token refresh in Databricks connectors is to follow a standardized blueprint that includes per-connection auth isolation and dedicated token handling validation patterns. This ensures reliable authentication deployments.

Why do I need per-connection auth isolation in my connector?▼

Per-connection auth isolation is needed in a Databricks connector to prevent token cross-contamination between multiple users or service principals. This blueprint enforces isolation alongside centralized User-Agent strategy and validation patterns.

How do I implement telemetry and User-Agent handling for Databricks API operations?▼

Implement telemetry and User-Agent handling by adopting a centralized User-Agent strategy within your connector blueprint. This ensures consistent operation structure and telemetry tracking across all REST or Python SDK API calls.

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