Auto-generated Data Documentation
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
What you'll learn
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
Tutorial
Key Features
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.
How to Use
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.
Related Documentation
Summary
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.
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