Quick Start¶

Get your first data pipeline running in under 10 minutes.

Prerequisites¶

Before starting, make sure you have:

Installed Dango (Installation Guide)
Python 3.10+ and Docker Desktop running
Virtual environment activated (if using venv)

Step 1: Initialize Your Project¶

If you haven't already initialized Dango:

macOS / LinuxWindows

cd my-analytics
source venv/bin/activate
dango init

cd my-analytics
.\venv\Scripts\Activate.ps1
dango init

The interactive wizard will guide you through:

Project name and configuration
Initial data source setup (optional)
Directory structure creation

Step 2: Add a Data Source¶

Let's add your first data source. Dango supports CSV files and 29+ verified dlt sources.

Option A: CSV File (Simplest)¶

dango source add

Follow the prompts:

Select CSV as the source type
Provide a path to your CSV file
Give it a descriptive name (e.g., sales_data)

Example:

$ dango source add
? Select source type: CSV
? CSV file path: /path/to/your/data.csv
? Source name: sales_data
✓ CSV source 'sales_data' added successfully

Option B: Stripe (API Integration)¶

For a more advanced example, try Stripe:

dango source add

Follow the prompts:

Select Stripe as the source type
Enter your Stripe API key (get it from Stripe Dashboard)
Give it a descriptive name (e.g., stripe_payments)

Option C: Google Sheets (OAuth)¶

dango source add

Follow the prompts:

Select Google Sheets as the source type
Complete OAuth authentication in your browser
Provide the Google Sheet URL
Give it a descriptive name (e.g., marketing_data)

Step 3: Sync Your Data¶

Now let's pull data from your source into DuckDB:

dango sync

What happens during sync:

dlt connects to your data source
Data is loaded into the raw schema in DuckDB
dbt generates staging models automatically
Transformations run to create clean, deduplicated data

Example output:

$ dango sync
[18:30:45] Starting sync for all sources...
[18:30:46] → sales_data: Extracting data...
[18:30:47] → sales_data: Loading to DuckDB...
[18:30:48] → sales_data: 1,234 rows loaded
[18:30:49] Running dbt transformations...
[18:30:51] ✓ 3 models completed successfully
[18:30:51] ✓ Sync completed in 6.2s

Dry Run (Preview Without Executing)¶

To preview what will happen without executing:

dango sync --dry-run

Step 4: Start the Platform¶

Start the Web UI, Metabase, and dbt docs server:

dango start

What starts:

Web UI - http://localhost:8800
Metabase - Accessible through Web UI
dbt docs - Accessible through Web UI

Example output:

$ dango start
[18:31:00] Starting Dango platform...
[18:31:02] ✓ Docker containers started
[18:31:05] ✓ Metabase ready
[18:31:06] ✓ Web UI ready at http://localhost:8800
[18:31:06] ✓ Platform started successfully

Open the Dashboard¶

macOS / LinuxWindows

open http://localhost:8800

Start-Process http://localhost:8800

Or simply visit http://localhost:8800 in your browser.

Step 5: Explore Your Data¶

Web UI (http://localhost:8800)¶

The Web UI provides:

Pipeline Status - See all your data sources and their sync status
Data Sources - Add, edit, and manage sources
Transformations - View and manage dbt models
Metabase - Access dashboards (link in Web UI)
dbt docs - Explore your data models (link in Web UI)

Metabase Dashboards¶

Click "Open Metabase" in the Web UI
Metabase is auto-configured with your DuckDB database
Start exploring your data with SQL or visual query builder

First time setup:

No login required (auto-configured)
All tables are already connected
Start creating dashboards immediately

Query Your Data with SQL¶

You can also query DuckDB directly:

dango query "SELECT * FROM marts.dim_customers LIMIT 10"

Or open an interactive SQL session:

dango query --interactive

Step 6: Add Transformations¶

Dango auto-generates staging models, but you can add your own transformations:

Create a New dbt Model¶

Navigate to your dbt models directory:
```
cd dbt_project/models/
```

Create a new model file (e.g., marts/revenue_summary.sql):

{{ config(materialized='table') }}

SELECT
    DATE_TRUNC('month', order_date) AS month,
    SUM(amount) AS total_revenue,
    COUNT(DISTINCT customer_id) AS unique_customers
FROM {{ ref('stg_sales_data') }}
GROUP BY 1
ORDER BY 1 DESC

Run dbt to materialize your model:
```
dango sync
```

Your new model is now available in DuckDB and Metabase!

Step 7: Automate with File Watcher¶

Enable automatic syncing when data files change:

dango watch

What it does:

Monitors CSV files for changes
Automatically runs dango sync when changes detected
Keeps your data up-to-date in real-time

Press Ctrl+C to stop watching.

Common Workflows¶

Daily Data Pipeline¶

# Morning routine
source venv/bin/activate
dango sync                    # Pull fresh data
dango start                   # Start dashboards

Development Workflow¶

# Make changes to dbt models
cd dbt_project/models/

# Test your changes
dango sync --dry-run          # Preview changes
dango sync                    # Apply changes

# View results in Metabase
open http://localhost:8800

Adding More Sources¶

# Add another source
dango source add

# Sync all sources
dango sync

# Sync specific source only
dango sync --source stripe_payments

Verify Everything Works¶

Let's make sure your setup is complete:

# Check Dango version
dango --version

# Validate installation
dango validate

# Check sync status
dango status

# List all sources
dango source list

Next Steps¶

Now that you have a working pipeline:

Core Concepts - Understand Dango's architecture
Data Sources - Connect more data sources
Transformations - Write advanced dbt models
Dashboards - Build Metabase dashboards
Web UI & CLI - Explore all commands

Troubleshooting¶

"dango: command not found"¶

Make sure your virtual environment is activated:

macOS / LinuxWindows

source venv/bin/activate

.\venv\Scripts\Activate.ps1

"Docker not running"¶

Start Docker Desktop and verify:

docker --version

"Port 8800 already in use"¶

Stop any running Dango instances:

dango stop

Or kill the process using the port:

lsof -ti:8800 | xargs kill -9

More Issues?¶

Check the full Troubleshooting Guide or open an issue.

Summary¶

You've successfully:

✅ Initialized a Dango project
✅ Added a data source
✅ Synced data to DuckDB
✅ Started the Web UI and Metabase
✅ Explored your data

Keep learning:

Explore the CLI Reference for all commands
Learn about Data Sources
Master dbt Transformations