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Your First Pipeline

A step-by-step walkthrough from empty project to scheduled dashboard — using Google Sheets as an example.

What You'll Build

By the end of this tutorial, you'll have:

  • A Dango project connected to a Google Sheets data source
  • Data synced into DuckDB
  • A dbt model transforming your data
  • A Metabase dashboard visualizing results
  • A scheduled sync keeping everything up to date

Time: ~20 minutes (mostly waiting for OAuth setup in Google Cloud Console)

Prerequisites

  • Dango installed (Installation Guide)
  • Python 3.10+ and Docker Desktop running
  • A Google account with a Google Sheet containing data
  • Virtual environment activated

Don't have a Google Sheet handy?

Create one with sample data — any spreadsheet with headers in row 1 works. For example, a sales tracker with columns: date, product, quantity, revenue.

Step 1: Initialize Your Project

dango init my-analytics
cd my-analytics

The wizard asks for an admin password — this protects the Web UI and Metabase. Pick something memorable for local development.

Your project structure:

my-analytics/
├── .dango/           # Configuration
├── .dlt/             # dlt credentials (gitignored)
├── dbt/              # Transformation models
└── data/             # DuckDB warehouse

Step 2: Add a Google Sheets Source

dango source add

The interactive wizard walks you through setup. For Google Sheets, the key prompts are:

  1. Select source type → choose Google Sheets
  2. Source name → give it a name (e.g., sales_data)
  3. OAuth setup → the wizard opens your browser for Google login
  4. Spreadsheet URL → paste the URL of your Google Sheet

See Adding Sources for the full wizard reference.

OAuth: What to Expect

The wizard needs OAuth credentials to access your Google account. If you haven't set them up before:

  1. Go to Google Cloud Console
  2. Create a project (or use an existing one)
  3. Enable the Google Sheets API
  4. Configure the OAuth consent screen (External type, Testing mode is fine)
  5. Create OAuth client ID credentials (Desktop app)
  6. Enter the Client ID and Client Secret when the wizard asks

\"App not verified\" warning

When authorizing, Google shows a warning because your app is in testing mode. Click Advanced → Go to [your app] to continue. This is expected and safe for your own data.

See the OAuth Troubleshooting Guide if you hit issues.

Step 3: Sync Your Data

dango sync sales_data

Dango will:

  1. Fetch data from your Google Sheet via the Sheets API
  2. Load it into DuckDB (table: raw_sales_data.*)
  3. Auto-generate dbt staging models
  4. Run dbt transformations

You'll see progress output as each step completes. After sync:

# Check what was loaded
dango db status

Step 4: Start the Platform

dango start

This boots the full stack:

  • Metabase dashboard server (via Docker)
  • FastAPI web server (monitoring UI at http://localhost:8800)

Background process

On macOS, dango start runs in the background. Closing the terminal does not stop Dango — the process continues running. Run dango start again at any time to check status or restart if needed.

Wait for the "Platform is ready" message before continuing.

Step 5: Explore in Metabase

Open the Web UI:

http://localhost:8800

Click Open Metabase (or go directly to http://localhost:3000). Log in with the admin credentials Dango set up automatically.

In Metabase:

  1. Click New → Question
  2. Pick your table (under the schema matching your source name)
  3. Build a visualization — try a bar chart of revenue by product

Create a Dashboard

  1. Click New → Dashboard
  2. Give it a name (e.g., "Sales Overview")
  3. Click + to add your question(s)
  4. Arrange and resize cards
  5. Click Save

Save Dashboard Config

Export your dashboard to version-controllable YAML:

dango metabase save

This creates files in metabase/ that you can commit to Git.

Step 6: Add a dbt Model

Create a custom model to transform your data:

dango model add

The wizard asks:

  1. Model name → e.g., monthly_revenue
  2. Source → select your sales_data source
  3. Layer → choose marts (for business-level aggregations)

This creates a SQL file at dbt/models/marts/monthly_revenue.sql. Edit it:

-- dbt/models/marts/monthly_revenue.sql
SELECT
    DATE_TRUNC('month', date) AS month,
    product,
    SUM(quantity) AS total_quantity,
    SUM(revenue) AS total_revenue
FROM {{ ref('stg_sales_data__sheet1') }}
GROUP BY 1, 2
ORDER BY 1 DESC

Run the model:

dango run

Your new monthly_revenue table now appears in Metabase for dashboarding.

Step 7: Schedule Automatic Syncs

Keep your data fresh with a scheduled sync:

dango schedule add

The wizard asks:

  1. Source → select sales_data
  2. Frequency → e.g., daily at 8:00 AM
  3. Include dbt? → Yes (runs transformations after sync)

Verify the schedule:

dango schedule status

Your pipeline is now fully automated — data syncs daily, transformations run, and Metabase dashboards update automatically.

What You've Built

Google Sheets → dlt sync → DuckDB → dbt models → Metabase dashboards
                  ↑
           Scheduled daily
Component What it does
dlt Extracts data from Google Sheets into DuckDB
DuckDB Stores all your data locally (single file)
dbt Transforms raw data into analysis-ready models
Metabase Visualizes data as interactive dashboards
APScheduler Runs syncs on a cron schedule

Next Steps