Auto-generated Data Documentation
Last updated
Last updated
In this guide, you'll learn how to use Paradime's Catalog feature, powered by DinoAI, to effortlessly create and maintain high-quality documentation for your data assets. Well-documented assets are crucial for clarity and collaboration, and the Catalog feature helps you comprehensively document your dbt™ models and their columns, ensuring consistency across your project.
Estimated completion time: 10 minutes
In this guide, you'll learn how to:
Access and navigate the Catalog feature
View and edit model documentation
Use DinoAI for auto-generating documentation
Review and customize generated content
Model Classification: Tag and categorize your models for easy organization.
Model Description: Add detailed descriptions to explain the purpose and logic of each model.
Model Column Details: Document metadata for each column, including type, tests, and classification.
DinoAI Auto-generation: Automatically generate context-specific descriptions for models and columns.
Auto-updating YAML files: All documentation updated via the Catalog UI will immediately be reflected in your project's corresponding .yaml files.
Access the Catalog:
Open the Command Panel within the Code IDE (located at the bottom of the screen)
Click on the "Catalog" tab.
View and Edit Model Information:
Click on any model in your project to view it's documentation.
Option: Click "Expand" icon on the top right of Catalog tab to expand view.
Click "Edit" Icon (✎) to edit any model classifications, description, column details, etc.
Use DinoAI for Auto-generation:
In the Catalog tab, click "Autogenerate" at the top left.
DinoAI will generate a detailed, context-specific model model and column descriptions.
Click "Edit" Icon (✎) to edit AI generated descriptions.
Review and Customize Generated Content:
Make any necessary edits to tailor the documentation to your specific needs.
Save your changes.
You've learned how to use Paradime's Catalog feature to autogenerate and maintain high-quality documentation for your dbt™ models. By leveraging this feature, you can significantly speed up your documentation process, ensure consistency across your project, and maintain up-to-date documentation as your models evolve.
Next, we'll explore how to enforce SQL and YAML best practices to further improve your code quality.