Paradime Help Docs
Get Started
  • 🚀Introduction
  • 📃Guides
    • Paradime 101
      • Getting Started with your Paradime Workspace
        • Creating a Workspace
        • Setting Up Data Warehouse Connections
        • Managing workspace configurations
        • Managing Users in the Workspace
      • Getting Started with the Paradime IDE
        • Setting Up a dbt™ Project
        • Creating a dbt™ Model
        • Data Exploration in the Code IDE
        • DinoAI: Accelerating Your Analytics Engineering Workflow
          • DinoAI Agent
            • Creating dbt Sources from Data Warehouse
            • Generating Base Models
            • Building Intermediate/Marts Models
            • Documentation Generation
            • Data Pipeline Configuration
            • Using .dinorules to Tailor Your AI Experience
          • Accelerating GitOps
          • Accelerating Data Governance
          • Accelerating dbt™ Development
        • Utilizing Advanced Developer Features
          • Visualize Data Lineage
          • Auto-generated Data Documentation
          • Enforce SQL and YAML Best Practices
          • Working with CSV Files
      • Managing dbt™ Schedules with Bolt
        • Creating Bolt Schedules
        • Understanding schedule types and triggers
        • Viewing Run History and Analytics
        • Setting Up Notifications
        • Debugging Failed Runs
    • Migrating from dbt™ cloud to Paradime
  • 🔍Concepts
    • Working with Git
      • Git Lite
      • Git Advanced
      • Read Only Branches
      • Delete Branches
      • Merge Conflicts
      • Configuring Signed Commits on Paradime with SSH Keys
    • dbt™ fundamentals
      • Getting started with dbt™
        • Introduction
        • Project Strucuture
        • Working with Sources
        • Testing Data Quality
        • Models and Transformations
      • Configuring your dbt™ Project
        • Setting up your dbt_project.yml
        • Defining Your Sources in sources.yml
        • Testing Source Freshness
        • Unit Testing
        • Working with Tags
        • Managing Seeds
        • Environment Management
        • Variables and Parameters
        • Macros
        • Custom Tests
        • Hooks & Operational Tasks
        • Packages
      • Model Materializations
        • Table Materialization
        • View​ Materialization
        • Incremental Materialization
          • Using Merge for Incremental Models
          • Using Delete+Insert for Incremental Models
          • Using Append for Incremental Models
          • Using Microbatch for Incremental Models
        • Ephemeral Materialization
        • Snapshots
      • Running dbt™
        • Mastering the dbt™ CLI
          • Commands
          • Methods
          • Selector Methods
          • Graph Operators
    • Paradime fundamentals
      • Global Search
        • Paradime Apps Navigation
        • Invite users to your workspace
        • Search and preview Bolt schedules status
      • Using --defer in Paradime
      • Workspaces and data mesh
    • Data Warehouse essentials
      • BigQuery Multi-Project Service Account
  • 📖Documentation
    • DinoAI
      • Agent Mode
        • Use Cases
          • Creating Sources from your Warehouse
          • Generating dbt™ models
          • Fixing Errors with Jira
          • Researching with Perplexity
          • Providing Additional Context Using PDFs
      • Context
        • File Context
        • Directory Context
      • Tools and Features
        • Warehouse Tool
        • File System Tool
        • PDF Tool
        • Jira Tool
        • Perplexity Tool
        • Terminal Tool
        • Coming Soon Tools...
      • .dinorules
      • Ask Mode
      • Version Control
      • Production Pipelines
      • Data Documentation
    • Code IDE
      • User interface
        • Autocompletion
        • Context Menu
        • Flexible layout
        • "Peek" and "Go To" Definition
        • IDE preferences
        • Shortcuts
      • Left Panel
        • DinoAI Coplot
        • Search, Find, and Replace
        • Git Lite
        • Bookmarks
      • Command Panel
        • Data Explorer
        • Lineage
        • Catalog
        • Lint
      • Terminal
        • Running dbt™
        • Paradime CLI
      • Additional Features
        • Scratchpad
    • Bolt
      • Creating Schedules
        • 1. Schedule Settings
        • 2. Command Settings
          • dbt™ Commands
          • Python Scripts
          • Elementary Commands
          • Lightdash Commands
          • Tableau Workbook Refresh
          • Power BI Dataset Refresh
          • Paradime Bolt Schedule Toggle Commands
          • Monte Carlo Commands
        • 3. Trigger Types
        • 4. Notification Settings
        • Templates
          • Run and Test all your dbt™ Models
          • Snapshot Source Data Freshness
          • Build and Test Models with New Source Data
          • Test Code Changes On Pull Requests
          • Re-executes the last dbt™ command from the point of failure
          • Deploy Code Changes On Merge
          • Create Jira Tickets
          • Trigger Census Syncs
          • Trigger Hex Projects
          • Create Linear Issues
          • Create New Relic Incidents
          • Create Azure DevOps Items
        • Schedules as Code
      • Managing Schedules
        • Schedule Configurations
        • Viewing Run Log History
        • Analyzing Individual Run Details
          • Configuring Source Freshness
      • Bolt API
      • Special Environment Variables
        • Audit environment variables
        • Runtime environment variables
      • Integrations
        • Reverse ETL
          • Hightouch
        • Orchestration
          • Airflow
          • Azure Data Factory (ADF)
      • CI/CD
        • Turbo CI
          • Azure DevOps
          • BitBucket
          • GitHub
          • GitLab
          • Paradime Turbo CI Schema Cleanup
        • Continuous Deployment with Bolt
          • GitHub Native Continuous Deployment
          • Using Azure Pipelines
          • Using BitBucket Pipelines
          • Using GitLab Pipelines
        • Column-Level Lineage Diff
          • dbt™ mesh
          • Looker
          • Tableau
          • Thoughtspot
    • Radar
      • Get Started
      • Cost Management
        • Snowflake Cost Optimization
        • Snowflake Cost Monitoring
        • BigQuery Cost Monitoring
      • dbt™ Monitoring
        • Schedules Dashboard
        • Models Dashboard
        • Sources Dashboard
        • Tests Dashboard
      • Team Efficiency Tracking
      • Real-time Alerting
      • Looker Monitoring
    • Data Catalog
      • Data Assets
        • Looker assets
        • Tableau assets
        • Power BI assets
        • Sigma assets
        • ThoughtSpot assets
        • Fivetran assets
        • dbt™️ assets
      • Lineage
        • Search and Discovery
        • Filters and Nodes interaction
        • Nodes navigation
        • Canvas interactions
        • Compare Lineage version
    • Integrations
      • Dashboards
        • Sigma
        • ThoughtSpot (Beta)
        • Lightdash
        • Tableau
        • Looker
        • Power BI
        • Streamlit
      • Code IDE
        • Cube CLI
        • dbt™️ generator
        • Prettier
        • Harlequin
        • SQLFluff
        • Rainbow CSV
        • Mermaid
          • Architecture Diagrams
          • Block Diagrams Documentation
          • Class Diagrams
          • Entity Relationship Diagrams
          • Gantt Diagrams
          • GitGraph Diagrams
          • Mindmaps
          • Pie Chart Diagrams
          • Quadrant Charts
          • Requirement Diagrams
          • Sankey Diagrams
          • Sequence Diagrams
          • State Diagrams
          • Timeline Diagrams
          • User Journey Diagrams
          • XY Chart
          • ZenUML
        • pre-commit
          • Paradime Setup and Configuration
          • dbt™️-checkpoint hooks
            • dbt™️ Model checks
            • dbt™️ Script checks
            • dbt™️ Source checks
            • dbt™️ Macro checks
            • dbt™️ Modifiers
            • dbt™️ commands
            • dbt™️ checks
          • SQLFluff hooks
          • Prettier hooks
      • Observability
        • Elementary Data
          • Anomaly Detection Tests
            • Anomaly tests parameters
            • Volume anomalies
            • Freshness anomalies
            • Event freshness anomalies
            • Dimension anomalies
            • All columns anomalies
            • Column anomalies
          • Schema Tests
            • Schema changes
            • Schema changes from baseline
          • Sending alerts
            • Slack alerts
            • Microsoft Teams alerts
            • Alerts Configuration and Customization
          • Generate observability report
          • CLI commands and usage
        • Monte Carlo
      • Storage
        • Amazon S3
        • Snowflake Storage
      • Reverse ETL
        • Hightouch
      • CI/CD
        • GitHub
        • Spectacles
      • Notifications
        • Microsoft Teams
        • Slack
      • ETL
        • Fivetran
    • Security
      • Single Sign On (SSO)
        • Okta SSO
        • Azure AD SSO
        • Google SAML SSO
        • Google Workspace SSO
        • JumpCloud SSO
      • Audit Logs
      • Security model
      • Privacy model
      • FAQs
      • Trust Center
      • Security
    • Settings
      • Workspaces
      • Git Repositories
        • Importing a repository
          • Azure DevOps
          • BitBucket
          • GitHub
          • GitLab
        • Update connected git repository
      • Connections
        • Code IDE environment
          • Amazon Athena
          • BigQuery
          • Clickhouse
          • Databricks
          • Dremio
          • DuckDB
          • Firebolt
          • Microsoft Fabric
          • Microsoft SQL Server
          • MotherDuck
          • PostgreSQL
          • Redshift
          • Snowflake
          • Starburst/Trino
        • Scheduler environment
          • Amazon Athena
          • BigQuery
          • Clickhouse
          • Databricks
          • Dremio
          • DuckDB
          • Firebolt
          • Microsoft Fabric
          • Microsoft SQL Server
          • MotherDuck
          • PostgreSQL
          • Redshift
          • Snowflake
          • Starburst/Trino
        • Manage connections
          • Set alternative default connection
          • Delete connections
        • Cost connection
          • BigQuery cost connection
          • Snowflake cost connection
        • Connection Security
          • AWS PrivateLink
            • Snowflake PrivateLink
            • Redshift PrivateLink
          • BigQuery OAuth
          • Snowflake OAuth
        • Optional connection attributes
      • Notifications
      • dbt™
        • Upgrade dbt Core™ version
      • Users
        • Invite users
        • Manage Users
        • Enable Auto-join
        • Users and licences
        • Default Roles and Permissions
        • Role-based access control
      • Environment Variables
        • Bolt Schedules Environment Variables
        • Code IDE Environment Variables
  • 💻Developers
    • GraphQL API
      • Authentication
      • Examples
        • Audit Logs API
        • Bolt API
        • User Management API
        • Workspace Management API
    • Python SDK
      • Getting Started
      • Modules
        • Audit Log
        • Bolt
        • Lineage Diff
        • Custom Integration
        • User Management
        • Workspace Management
    • Paradime CLI
      • Getting Started
      • Bolt CLI
    • Webhooks
      • Getting Started
      • Custom Webhook Guides
        • Create an Azure DevOps Work item when a Bolt run complete with errors
        • Create a Linear Issue when a Bolt run complete with errors
        • Create a Jira Issue when a Bolt run complete with errors
        • Trigger a Slack notification when a Bolt run is overrunning
    • Virtual Environments
      • Using Poetry
      • Troubleshooting
    • API Keys
    • IP Restrictions in Paradime
    • Company & Workspace token
  • 🙌Best Practices
    • Data Mesh Setup
      • Configure Project dependencies
      • Model access
      • Model groups
  • ‼️Troubleshooting
    • Errors
    • Error List
    • Restart Code IDE
  • 🔗Other Links
    • Terms of Service
    • Privacy Policy
    • Paradime Blog
Powered by GitBook
On this page
  • What you'll learn
  • 1. Creating dbt™ Models
  • 2. Explaining dbt™ Models
  • 3. Debugging dbt™ Models
  • 4. Converting SQL to dbt™ Models
  • Summary

Was this helpful?

  1. Guides
  2. Paradime 101
  3. Getting Started with the Paradime IDE
  4. DinoAI: Accelerating Your Analytics Engineering Workflow

Accelerating dbt™ Development

PreviousAccelerating Data GovernanceNextUtilizing Advanced Developer Features

Last updated 8 months ago

Was this helpful?

DinoAI enhances your dbt™ development by automating tasks such as model creation, explanation, debugging, and SQL-to-dbt™ conversion. This guide will help you leverage DinoAI to speed up your dbt™ development process, ensuring your models are well-structured, accurate, and aligned with your project standards.

Estimated completion time: 15 minutes

Prerequisites

  • Basic understanding of dbt™ concepts and SQL


What you'll learn

In this guide, you'll learn how to use DinoAI for:


1. Creating dbt™ Models

Creating dbt™ models is a foundational step in any dbt™ project. DinoAI simplifies this process by generating model code based on your prompts, ensuring consistency and saving time across your project.

How to use:

  1. Open DinoAI: Click the Dino AI icon (🪄) on the left side of the Editor.

  2. Access the Create Model Feature: Select the "One Click" command "Create a dbt model", or type "/model" in the prompt.

  3. Describe Your Model: Enter a detailed prompt for the dbt model you'd like to create. For example:

/model Create a dbt model named int_nba_player_info that joins all columns from nba_player_info with the salary and season columns from nba_player_salaries, using the player_id column as the join key. Materialize it as a view.

  1. Review Generated Code: Carefully examine the AI-generated model code.

  2. Implement the Model: Copy the generated code and paste it into the appropriate .sql file in your project.

  3. Refine as Needed: Modify the generated code to meet your specific requirements and project standards.


2. Explaining dbt™ Models

Understanding complex dbt™ models is essential for maintaining and collaborating on your data projects. DinoAI provides detailed explanations of your dbt™ models, breaking down their purpose, structure, and key components.

How to get started:

  1. Open DinoAI: Click the Dino AI icon (🪄) on the left side of the Editor.

  2. Access the Explain Model Feature: Select the "One Click" command "Explain a dbt model", or type "/explain" in the prompt.

  3. Specify Your Model: Enter the name of the model you want explained. For example:

/Explain nba_player_info

  1. Review Explanation: Carefully read the AI-generated summary of your model's purpose, output, and explanations of key parts like CTEs and subqueries.

Alternative method to access the Copilot's '/Explain' command:

  1. Right-click a .sql file in the project folder, files tab, or open file.

  2. In the DinoAI Copilot dropdown, select "Explain model".


3. Debugging dbt™ Models

Ensuring that your dbt™ models are free of errors is critical for the reliability of your data pipeline. DinoAI assists in debugging by identifying issues in your models and providing fixes.

How to use:

  1. Open DinoAI: Click the Dino AI icon (🪄) on the left side of the Editor.

  2. Access the Debug Feature: Select the "One Click" command "Debug a dbt model", or type "/fix" in the prompt.

  3. Specify Your Model: Enter the name of the model you want to debug. For example:

/Fix @nba_player_info

  1. Review Changes: Carefully examine the summary of changes made and the full, debugged code.

  2. Implement Fixes: Copy the debugged code and paste it into your project's appropriate .sql file.

  3. Verify: Use the Data Explorer to ensure the fixes work as expected.

Alternative method to access the Copilot's '/Explain' command:

  1. Right-click a .sql file in the project folder, files tab, or open file.

  2. In the DinoAI Copilot dropdown, select "Fix model".


4. Converting SQL to dbt™ Models

Converting existing SQL queries into dbt™ models can save significant development time. DinoAI automates this conversion process, allowing you to quickly transition from raw queries to structured dbt™ models.

How to use:

  1. Right-click a .sql file: You can right click a .sql file from the project folder, the files tabs, or within an opened .sql file.

  2. Hover over the DinoAI Copilot dropdown and select option "Convert SQL to dbt model"

  3. Review Generated Code: Carefully examine the AI-generated model code.

  4. Implement the Model: Copy the generated code and paste it into the appropriate .sql file in your project.

  5. Refine as Needed: Modify the generated code to meet your specific requirements and project standards.

  6. Verify: Use the Data Explorer to ensure the model output is as expected.

Alternative method to access the Copilot's '/sql_to_dbt' command:

  1. Right-click a .sql file in the project folder, files tab, or open file.

  2. In the DinoAI Copilot dropdown, select "Convert SQL to dbt model".


Summary

You've learned how to use DinoAI to accelerate your dbt™ development through model creation, explanation, debugging, and SQL conversion. These features speed up your workflow, enhance code quality, and improve collaboration, allowing you to focus more on data strategy.

Next, we'll learn about some to further improve your dbt™ development process.

📃
advanced Developer features in the Code IDE
Creating dbt™ Models
Explaining dbt™ Models
Debugging dbt™ Models
Converting SQL to dbt™ Models