atlan-sql-connector-patterns

Select and implement SQL connector patterns for metadata extraction workflows.

4|7|Updated Apr 30, 2025

Search Tags:#sdk #metadata #sql #postgres #redshift #connector-patterns

Ready to run

npx skills add https://github.com/atlanhq/atlan-sample-apps --skill atlan-sql-connector-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: atlan-sql-connector-patterns
Source: https://github.com/atlanhq/atlan-sample-apps/tree/main/.agents/skills/atlan-sql-connector-patterns
Command: npx skills add https://github.com/atlanhq/atlan-sample-apps --skill atlan-sql-connector-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams select and implement the most appropriate SQL connector pattern (SDK-default minimal or source-specific custom) for building or extending SQL metadata and query extraction connectors.

Core Features & Use Cases

Pattern selection guidance: Decide between postgres-minimal and redshift-custom paths based on requirements.
Implementation roadmap: Provide required components, verification steps, and handoff for contract validators.
Use Case: When adding a new SQL source, use this Skill to decide and document the connector strategy, ensuring SDK defaults are used unless a custom path is justified.

Quick Start

Run through the workflow to choose a path, implement components, and verify preflight and transformation behavior against references.

Frequently Asked Questions about atlan-sql-connector-patterns

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

FAQPage Schema

How do I choose between postgres-minimal and redshift-custom patterns for SQL metadata extraction?▼

SQL connector patterns depend on your source requirements. Postgres-minimal uses SDK defaults for standard SQL sources, while redshift-custom implements source-specific logic. Choose redshift-custom only when your SQL source requires custom authentication, preflight checks, or transformation behavior beyond the SDK baseline.

What steps do I need to follow to implement a SQL connector pattern?▼

Implementation requires selecting your pattern, documenting the rationale, verifying authentication and preflight checks, mapping workflow components, defining transformations, and running contract validators. This ensures your connector adheres to SDK standards and handles your source correctly.

When should I build a custom SQL connector instead of using the minimal pattern?▼

Use a custom connector when your SQL source has specialized requirements: non-standard authentication, custom preflight validation, source-specific query transformations, or unique metadata extraction logic. The minimal pattern works for standard Postgres-compatible sources without these requirements.

Can I use SQL connector patterns to add a new database source?▼

Yes. SQL connector patterns guide selection and implementation for adding new SQL sources—whether databases like Postgres, Redshift, or others. Document your pattern choice, implement required components, and validate behavior against reference specifications and contract validators.

What happens if authentication or preflight checks fail in my SQL connector?▼

Failed authentication or preflight checks indicate your connector configuration or source environment needs adjustment. The pattern requires documenting and verifying these checks before proceeding. Contract validators catch misconfigurations early, preventing metadata extraction failures downstream.

How do I verify my SQL connector implementation is correct?▼

Verify by running contract validators against your connector, testing transformation behavior against reference maps, and confirming preflight checks and authentication succeed. These validation steps ensure your chosen pattern meets SDK standards and your source integrates correctly.