CLI¶

Command-line interface for managing your Dango data platform.

Overview¶

The Dango CLI provides powerful command-line tools for project management, data syncing, and transformation workflows. Automate your data pipelines with scriptable commands and integrate seamlessly into CI/CD systems.

Key Commands:

dango init - Initialize new projects
dango sync - Load data from sources
dango generate - Auto-generate staging models
dango run - Execute dbt transformations
dango start/stop - Manage platform services
dango validate - Check configuration health

Getting Started¶

Installation¶

Install Dango CLI via pip:

pip install getdango

Verify installation:

dango --version
# Output: dango version 0.0.5

Create Your First Project¶

# Initialize project
dango init my-analytics
cd my-analytics

# Start platform
dango start

# Add a data source
dango source add

# Sync data
dango sync

# Generate staging models
dango generate

# Run transformations
dango run

Get Help¶

# General help
dango --help

# Command-specific help
dango sync --help
dango source add --help

CLI Guides¶

CLI Reference

Complete command reference for all dango commands.
- Global options and flags
- Project management commands
- Data operation commands
- Source management commands
- Platform control commands
- Configuration commands
CLI Reference
Init & Start

Project initialization and starting services.
- Initialize new projects
- Project configuration
- Start/stop/restart platform
- Manage multiple projects
- Troubleshoot startup issues
Init & Start Guide
Sync & Run

Data syncing and running transformations.
- Sync data from sources
- Generate staging models
- Run dbt transformations
- Incremental vs full-refresh
- Performance optimization
- Troubleshoot failures
Sync & Run Guide
Source Management

CLI commands for managing data sources.
- Add sources interactively
- List and inspect sources
- Edit configurations
- Manage OAuth credentials
- Import/export sources
Source Management
Validation

Validate configuration and troubleshoot issues.
- Validate project configuration
- Check source connections
- Verify database health
- Auto-fix common issues
- CI/CD integration
Validation Guide

Command Categories¶

Project Lifecycle¶

Manage Dango projects:

dango init my-project       # Create new project
dango info                  # Show project metadata
dango status                # Check if running
dango validate              # Verify configuration
dango rename new-name       # Rename project

Data Operations¶

Load and transform data:

dango sync                  # Sync all sources
dango sync --source stripe  # Sync specific source
dango generate              # Auto-generate staging
dango run                   # Run dbt transformations
dango run --select marts.*  # Run specific models

Source Management¶

Configure data sources:

dango source add            # Add new source (wizard)
dango source list           # List all sources
dango source remove name    # Remove source
dango auth list             # List OAuth credentials
dango auth refresh name     # Refresh OAuth token

Platform Control¶

Start and stop services:

dango start                 # Start all services
dango stop                  # Stop all services
dango restart               # Restart platform
dango start --no-metabase   # Skip Metabase

Database Operations¶

Manage the data warehouse:

dango db status             # Database info
dango db clean              # Remove orphaned tables

Documentation¶

Generate dbt documentation:

dango docs                  # Generate and serve docs
dango docs --port 8082      # Custom port

Quick Reference¶

Essential Workflow¶

# 1. Create project
dango init my-analytics
cd my-analytics

# 2. Add data source
dango source add

# 3. Start platform
dango start

# 4. Sync data
dango sync

# 5. Generate staging
dango generate

# 6. Run transformations
dango run

# 7. View data
# Open http://localhost:3000 (Metabase)

Daily Operations¶

# Morning data refresh
dango sync && dango run

# Check what changed
dango status

# View recent activity
dango source list

# Validate everything
dango validate

Development Iteration¶

# Edit dbt model
vim dbt/models/marts/my_metric.sql

# Run just that model
dango run --select my_metric

# Check results
duckdb data/warehouse.duckdb "SELECT * FROM marts.my_metric LIMIT 10"

Common Patterns¶

Sync Specific Sources¶

# Single source
dango sync --source stripe_payments

# Multiple sources
dango sync --source stripe --source google_sheets

# All except one
dango sync --exclude old_source

Run Specific Models¶

# Single model
dango run --select customer_metrics

# Model and downstream
dango run --select customer_metrics+

# Model and upstream
dango run --select +customer_metrics

# Entire directory
dango run --select marts.*

# By tag
dango run --select tag:daily

Full Refresh Workflows¶

# Full refresh data
dango sync --full-refresh

# Full refresh dbt
dango run --full-refresh

# Both
dango sync --full-refresh && dango run --full-refresh

Scripting and Automation¶

Shell Scripts¶

Daily sync script (daily-sync.sh):

#!/bin/bash
set -e

echo "Starting daily sync at $(date)"

# Validate configuration
dango validate --strict

# Sync all sources
dango sync

# Generate staging (if schema changed)
dango generate

# Run transformations
dango run

# Run tests
dbt test --profiles-dir dbt --project-dir dbt

echo "Daily sync complete at $(date)"

Make executable:

chmod +x daily-sync.sh

Schedule with cron (6am daily):

0 6 * * * cd /path/to/project && ./daily-sync.sh >> /var/log/dango-sync.log 2>&1

Error Handling¶

#!/bin/bash

# Exit on error
set -e

# Trap errors
trap 'echo "Error on line $LINENO"' ERR

# Run with error checking
dango sync || {
  echo "Sync failed! Sending alert..."
  curl -X POST https://hooks.slack.com/... \
    -d '{"text": "Dango sync failed!"}'
  exit 1
}

echo "Success!"

Parallel Execution¶

#!/bin/bash

# Sync sources in parallel
dango sync --source stripe &
dango sync --source google_sheets &
dango sync --source sales_data &

# Wait for all to complete
wait

echo "All syncs complete"

CI/CD Integration¶

GitHub Actions¶

.github/workflows/dango.yml:

name: Dango Pipeline

on:
  schedule:
    - cron: '0 6 * * *'  # 6am daily
  workflow_dispatch:

jobs:
  sync-and-transform:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'

      - name: Install Dango
        run: pip install getdango

      - name: Validate Configuration
        run: dango validate --strict
        env:
          STRIPE_API_KEY: ${{ secrets.STRIPE_API_KEY }}

      - name: Sync Data
        run: dango sync
        env:
          STRIPE_API_KEY: ${{ secrets.STRIPE_API_KEY }}

      - name: Generate Staging
        run: dango generate

      - name: Run Transformations
        run: dango run

      - name: Run Tests
        run: dbt test --profiles-dir dbt --project-dir dbt

      - name: Upload Database
        uses: actions/upload-artifact@v3
        with:
          name: warehouse
          path: data/warehouse.duckdb

GitLab CI¶

.gitlab-ci.yml:

stages:
  - validate
  - sync
  - transform
  - test

validate:
  stage: validate
  script:
    - pip install getdango
    - dango validate --strict

sync:
  stage: sync
  script:
    - pip install getdango
    - dango sync
  artifacts:
    paths:
      - data/warehouse.duckdb

transform:
  stage: transform
  script:
    - pip install getdango
    - dango generate
    - dango run
  dependencies:
    - sync

test:
  stage: test
  script:
    - dbt test --profiles-dir dbt --project-dir dbt
  dependencies:
    - transform

Environment Variables¶

Configuration¶

Set environment variables for credentials:

# .env file (gitignored)
STRIPE_API_KEY=sk_live_abc123...
GOOGLE_CREDENTIALS=/path/to/credentials.json
POSTGRES_URL=postgresql://user:pass@host:5432/db
FACEBOOK_ACCESS_TOKEN=EAA...

Load in shell:

# Export for current session
export $(cat .env | xargs)

# Or use direnv (auto-loads .env)
direnv allow

Common Variables¶

Variable	Purpose
`DANGO_PROJECT_DIR`	Override project directory
`DANGO_LOG_LEVEL`	Set log verbosity (DEBUG, INFO, WARNING, ERROR)
`DANGO_NO_COLOR`	Disable colored output
`STRIPE_API_KEY`	Stripe API authentication
`GOOGLE_CREDENTIALS`	Google API service account
`POSTGRES_URL`	PostgreSQL connection string

Tips and Tricks¶

Aliases¶

Add to ~/.bashrc or ~/.zshrc:

# Quick commands
alias ds='dango sync'
alias dr='dango run'
alias dv='dango validate'
alias dsr='dango sync && dango run'

# Common options
alias dsf='dango sync --full-refresh'
alias drf='dango run --full-refresh'

Tab Completion¶

Enable bash completion:

# Add to ~/.bashrc
eval "$(_DANGO_COMPLETE=bash_source dango)"

# Or for zsh (~/.zshrc)
eval "$(_DANGO_COMPLETE=zsh_source dango)"

Watch Mode¶

Auto-run on file changes:

# Install watchdog
pip install watchdog

# Watch dbt models
watchmedo shell-command \
  --patterns="*.sql" \
  --recursive \
  --command='dango run --select marts.*' \
  dbt/models/

CLI vs Web UI¶

Choose the right interface for your task:

Task	CLI	Web UI
Automation	✅ Excellent	❌ Not scriptable
Visual monitoring	⚠️ Terminal only	✅ Dashboards
File uploads	⚠️ Copy files	✅ Drag-and-drop
Quick actions	✅ Fast commands	✅ One-click buttons
CI/CD pipelines	✅ Perfect	❌ No integration
Debugging	✅ Verbose logs	✅ Search/filter logs
Remote access	✅ SSH compatible	⚠️ Needs port forwarding

Both are equally powerful - use what fits your workflow!

Next Steps¶

CLI Reference

Complete reference for all CLI commands and options.

CLI Reference
Init & Start

Learn project initialization and service management.

Init & Start Guide
Sync & Run

Master data syncing and transformation workflows.

Sync & Run Guide
Web UI Alternative

Explore the visual interface for the same functionality.

Web UI Overview