Project Structure¶

Understanding the directory layout and configuration files.

Overview¶

A Dango project has a standard directory structure created by dango init:

my-analytics/
├── .dango/                    # Project configuration
├── .dlt/                      # dlt configuration
├── data/                      # Database and uploads
├── dbt/                       # dbt project
├── custom_sources/            # Custom dlt sources
├── dashboards/                # Metabase exports (optional)
├── docker-compose.yml         # Docker services
├── Dockerfile                 # Metabase DuckDB driver
├── .gitignore                 # Git ignore patterns
├── .env                       # Environment variables
└── README.md                  # Auto-generated docs

.dango/ - Project Configuration¶

The .dango/ directory contains all Dango-specific configuration.

Directory Contents¶

.dango/
├── project.yml       # Project metadata and platform settings
├── sources.yml       # Data source definitions
├── routing.json      # Multi-project domain routing (auto-generated)
└── metabase.yml      # Metabase credentials (auto-generated)

What to commit: project.yml, sources.yml What to ignore: routing.json, metabase.yml

project.yml¶

Project metadata and platform configuration.

Location: .dango/project.yml

Example:

project:
  name: my-analytics
  organization: Acme Corp
  created: 2024-11-15
  created_by: alice@acme.com
  purpose: Track daily sales and customer behavior
  dango_version: 0.0.5  # Dango version this project was created with

  stakeholders:
    - name: Bob Chen
      role: CEO - Executive view
      contact: bob@acme.com
    - name: Sarah Lee
      role: Analyst - Power user
      contact: sarah@acme.com

  sla: "Daily by 9am UTC"
  limitations: "Stripe data has 24h delay. GA4 takes 48h to backfill."
  getting_started: |
    1. Run: dango sync
    2. Open: http://localhost:8800

platform:
  port: 8800                     # Web UI port
  metabase_port: 3000           # Metabase BI port
  dbt_docs_port: 8081           # dbt docs port

  auto_sync: true               # Enable file watcher
  auto_dbt: true                # Auto-run dbt after sync
  debounce_seconds: 600         # Wait 10 min before triggering

  watch_patterns:
    - "*.csv"
    - "*.xlsx"
  watch_directories:
    - "data/uploads"

  duckdb_path: "./data/warehouse.duckdb"
  dbt_project_dir: "./dbt"
  data_dir: "./data"

Key sections:

project - Human-readable metadata
stakeholders - Who uses this project
sla - Data freshness expectations
limitations - Known data delays or gaps
platform - Technical configuration

sources.yml¶

Data source definitions.

Location: .dango/sources.yml

Example:

version: "1.0"
sources:
  # CSV source
  - name: orders
    type: csv
    enabled: true
    csv:
      directory: ./data/uploads
      file_pattern: "orders_*.csv"
      deduplication_strategy: latest_only
      primary_key: order_id
      timestamp_column: created_at
      timestamp_sort: desc

  # Stripe API source
  - name: stripe_payments
    type: stripe
    enabled: true
    stripe:
      stripe_secret_key_env: STRIPE_API_KEY
      start_date: 2024-01-01

  # Google Sheets source (OAuth)
  - name: forecast
    type: google_sheets
    enabled: true
    google_sheets:
      spreadsheet_url_or_id: "1a2b3c4d5e6f..."
      range_names: [Sheet1, Q4_Planning]

  # Custom dlt_native source
  - name: internal_api
    type: dlt_native
    enabled: true
    dlt_native:
      source_module: my_custom_source
      source_function: internal_api_source
      function_kwargs:
        api_url: "https://api.internal.com"
        api_key_env: INTERNAL_API_KEY

  # Disabled source
  - name: old_hubspot
    type: hubspot
    enabled: false

Common fields:

name - Unique identifier (used in table names)
type - Source type (csv, stripe, hubspot, etc.)
enabled - Whether to sync this source
<type> - Type-specific configuration

.dlt/ - dlt Configuration¶

The .dlt/ directory contains dlt pipeline configuration.

Directory Contents¶

.dlt/
├── config.toml       # Pipeline settings (destination, etc.)
└── secrets.toml      # API keys, OAuth tokens (git-ignored)

What to commit: config.toml What to ignore: secrets.toml

config.toml¶

dlt pipeline configuration.

Location: .dlt/config.toml

Auto-generated content:

[destination.duckdb]
credentials = "./data/warehouse.duckdb"

[runtime]
log_level = "INFO"

You rarely need to edit this - Dango manages it automatically.

secrets.toml¶

Sensitive credentials (API keys, OAuth tokens).

Location: .dlt/secrets.toml

Example:

[sources.stripe]
stripe_secret_key = "sk_live_abc123..."

[sources.google_analytics]
credentials = """
{
  "type": "service_account",
  "project_id": "my-ga-project",
  "private_key": "-----BEGIN PRIVATE KEY-----\n...",
  "client_email": "service@my-ga-project.iam.gserviceaccount.com"
}
"""

[sources.facebook_ads]
access_token = "EAABwzLix..."
account_id = "act_123456789"

Security: - Never commit this file (included in .gitignore) - Use .env or environment variables as alternative - dlt reads credentials in this priority order: 1. .dlt/secrets.toml 2. .env 3. Environment variables

data/ - Database and Uploads¶

The data/ directory stores the DuckDB database and CSV uploads.

Directory Contents¶

data/
├── warehouse.duckdb          # Main analytics database
├── warehouse.duckdb.wal      # Write-ahead log (temporary)
├── uploads/                  # Default CSV upload location
│   ├── orders_2024-12-01.csv
│   ├── orders_2024-12-02.csv
│   └── customers.csv
└── warehouse/                # Legacy data location (ignored)

What to commit: Nothing (all ignored by default) What to backup: warehouse.duckdb, uploads/

warehouse.duckdb¶

The DuckDB analytics database file.

Location: data/warehouse.duckdb

Size: Typically 10-500 MB for small projects

Contents: - All raw data ingested by dlt - Staging models (tables) - Intermediate models (tables) - Marts models (tables) - dlt metadata tables

Direct access:

# Open DuckDB CLI
duckdb data/warehouse.duckdb

# Run queries
SELECT * FROM marts.customer_metrics LIMIT 10;

Backup:

# Copy database file
cp data/warehouse.duckdb backups/warehouse-2024-12-01.duckdb

# Or export specific tables
duckdb data/warehouse.duckdb -c "EXPORT DATABASE 'export_dir'"

uploads/¶

Default location for CSV file uploads.

Location: data/uploads/

How it's used: 1. You drop CSV files here 2. File watcher detects changes (if auto_sync: true) 3. Dango auto-syncs after debounce period 4. Data lands in raw_{source}.* tables

File naming patterns:

# Match specific pattern
file_pattern: "orders_*.csv"
# → orders_2024-12-01.csv ✓
# → orders_2024-12-02.csv ✓
# → customers.csv ✗

# Match all CSVs
file_pattern: "*.csv"
# → Any .csv file ✓

dbt/ - dbt Project¶

The dbt/ directory contains all SQL transformation logic.

Directory Contents¶

dbt/
├── dbt_project.yml           # dbt project configuration
├── profiles.yml              # DuckDB connection profile
├── models/
│   ├── staging/              # Auto-generated staging models
│   │   ├── stg_stripe_charges.sql
│   │   ├── stg_stripe_customers.sql
│   │   ├── _stg_stripe__sources.yml
│   │   └── _stg_stripe__schema.yml
│   ├── intermediate/         # User-created business logic
│   │   ├── int_customer_ltv.sql
│   │   └── int_customer_segments.sql
│   └── marts/                # User-created final metrics
│       ├── customer_metrics.sql
│       ├── daily_sales.sql
│       └── schema.yml
├── macros/                   # Reusable SQL macros
├── tests/                    # Data tests
├── snapshots/                # SCD Type 2 snapshots
└── target/                   # Build artifacts (git-ignored)

What to commit: Everything except target/, logs/, dbt_packages/

dbt_project.yml¶

dbt project configuration.

Location: dbt/dbt_project.yml

Auto-generated content:

name: my_analytics
version: 1.0.0

profile: duckdb
model-paths: [models]
test-paths: [tests]
data-paths: [data]
macro-paths: [macros]
snapshot-paths: [snapshots]

target-path: target
clean-targets: [target, dbt_packages]

models:
  my_analytics:
    staging:
      materialized: table     # Tables for Metabase performance
      tags: [staging]
    intermediate:
      materialized: table     # Pre-computed for reuse
      tags: [intermediate]
    marts:
      materialized: table     # Optimized for BI queries
      tags: [marts]

profiles.yml¶

dbt DuckDB connection configuration.

Location: dbt/profiles.yml

Auto-generated content:

duckdb:
  target: dev
  outputs:
    dev:
      type: duckdb
      path: ../data/warehouse.duckdb
      threads: 4

Connects dbt to: data/warehouse.duckdb

models/ Structure¶

staging/¶

Auto-generated by dango sync.

You can customize these files by editing the generated SQL. Remove the auto-generated comment at the top to prevent Dango from overwriting your changes.

Contents: - stg_<source>__<table>.sql - Staging model SQL - _stg_<source>__sources.yml - Documents raw tables - _stg_<source>__schema.yml - Column descriptions

intermediate/¶

User-created reusable business logic.

You create these files.

Examples:

-- int_customer_ltv.sql
-- Reusable customer lifetime value calculation

-- int_customer_segments.sql
-- Customer segmentation logic

marts/¶

User-created final metrics for BI tools.

You create these files.

Examples:

-- customer_metrics.sql
-- Wide table with all customer KPIs

-- daily_sales.sql
-- Daily aggregated sales metrics

custom_sources/ - Custom dlt Sources¶

Write custom dlt sources without modifying Dango code.

Directory Contents¶

custom_sources/
├── my_api_source.py
├── internal_crm.py
└── legacy_system.py

What to commit: All .py files

Example Custom Source¶

File: custom_sources/my_api_source.py

import dlt
from typing import Iterator, Dict, Any

@dlt.resource
def my_api_source(api_key: str, endpoint: str) -> Iterator[Dict[str, Any]]:
    """
    Custom dlt source for internal API.
    """
    import requests

    headers = {"Authorization": f"Bearer {api_key}"}
    response = requests.get(endpoint, headers=headers)
    response.raise_for_status()

    for item in response.json():
        yield item

Configuration in sources.yml:

- name: my_api_data
  type: dlt_native
  enabled: true
  dlt_native:
    source_module: my_api_source        # Filename (without .py)
    source_function: my_api_source      # Function name
    function_kwargs:
      api_key_env: MY_API_KEY          # From .env or secrets.toml
      endpoint: "https://api.internal.com/data"

Learn more: Custom Sources guide

Docker Configuration¶

Docker Compose orchestrates Metabase and dbt-docs containers.

docker-compose.yml¶

Service definitions.

Location: docker-compose.yml

Auto-generated content:

ser

id=__codelineno-19-1 name=__codelineno-19-1 href=#__codelineno-19-1>version: '3.8' vices: metabase: image: metabase/metabase:latest container_name: dango-metabase-my-analytics ports: - "3000:3000" volumes: - ./data/warehouse.duckdb:/duckdb/warehouse.duckdb - ./metabase-plugins:/plugins environment: MB_DB_FILE: /metabase-data/metabase.db MB_PLUGINS_DIR: /plugins dbt-docs: image: fishtownanalytics/dbt:latest container_name: dango-dbt-docs-my-analytics ports: - "8081:8080" volumes: - ./dbt/target:/usr/app/target command: docs serve --port 8080

Dockerfile¶

Metabase with DuckDB driver.

Location: Dockerfile

Auto-generated content:

FROM metabase/metabase:latest

# Install DuckDB driver
COPY metabase-plugins/duckdb.metabase-driver.jar /plugins/

ENV MB_PLUGINS_DIR=/plugins

.gitignore¶

Git ignore patterns to protect secrets and large files.

Location: .gitignore

Auto-generated content:

# Secrets
.env
.dlt/secrets.toml
*.key
*.pem

# Data (large files)
data/
*.duckdb
*.duckdb.wal

# dbt artifacts
dbt/target/
dbt/logs/
dbt/dbt_packages/

# Python
__pycache__/
*.pyc
.venv/
venv/

# System
.DS_Store

What gets committed: - .dango/project.yml - .dango/sources.yml - dbt/models/ (your SQL) - custom_sources/ (your Python) - docker-compose.yml - README.md

What stays local: - data/warehouse.duckdb (database) - .env (secrets) - .dlt/secrets.toml (API keys)

.env - Environment Variables¶

Store secrets and environment-specific configuration.

Location: .env

Example:

# Stripe
STRIPE_API_KEY=sk_live_abc123...

# Google Analytics
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# HubSpot
HUBSPOT_API_KEY=pat-na1-xyz...

# Custom sources
MY_API_KEY=internal-api-key-here
INTERNAL_API_URL=https://api.internal.com

Security: - Never commit (in .gitignore) - Use for local development - For production, use proper secrets management

Precedence: 1. .dlt/secrets.toml (highest) 2. .env 3. System environment variables (lowest)

dashboards/ - Metabase Exports (Optional)¶

Store exported Metabase dashboard definitions.

Location: dashboards/

Example:

dashboards/
├── customer-overview.yml
├── daily-sales.yml
└── churn-analysis.yml

How to use: 1. Export dashboard from Metabase UI 2. Save YAML to dashboards/ 3. Commit to Git 4. Teammates import into their Metabase

Learn more: Metabase Overview

README.md¶

Auto-generated project documentation.

Location: README.md

Auto-generated on dango init:

# my-analytics

Track daily sales and customer behavior

## Quick Start

```bash
dango sync
dango start
```

Open: http://localhost:8800

## Stakeholders

- Bob Chen (CEO) - bob@acme.com
- Sarah Lee (Analyst) - sarah@acme.com

## SLA

Daily by 9am UTC

## Limitations

Stripe data has 24h delay. GA4 takes 48h to backfill.

File Size Reference¶

Typical file sizes for a small project:

File/Directory	Size	Notes
`data/warehouse.duckdb`	10-500 MB	Grows with data volume
`.dango/sources.yml`	1-5 KB	Depends on source count
`dbt/models/`	10-100 KB	Depends on model count
`docker-compose.yml`	1 KB	Static
Total project	50-1000 MB	Mostly database

Backup Strategy¶

What to backup: 1. Database: data/warehouse.duckdb 2. Configuration: .dango/, dbt/models/ 3. Custom code: custom_sources/ 4. Secrets: .env, .dlt/secrets.toml (separately!)

What NOT to backup: - dbt/target/ (regenerated) - Docker images (re-downloadable) - metabase-plugins/ (re-downloadable)

Multi-Project Setup¶

Running multiple Dango projects on the same machine:

~/analytics/
├── project-a/
│   ├── .dango/project.yml       # port: 8800
│   ├── data/warehouse.duckdb
│   └── dbt/
├── project-b/
│   ├── .dango/project.yml       # port: 8801
│   ├── data/warehouse.duckdb
│   └── dbt/
└── project-c/
    ├── .dango/project.yml       # port: 8802
    ├── data/warehouse.duckdb
    └── dbt/

Each project gets unique ports: - Project A: Web UI 8800, Metabase 3000, dbt-docs 8081 - Project B: Web UI 8801, Metabase 3001, dbt-docs 8082 - Project C: Web UI 8802, Metabase 3002, dbt-docs 8083

Manage all projects:

# Check all running projects
dango status --all

# Stop all projects
dango stop --all

Next Steps¶

Architecture - See how files relate to components
Data Layers - Understand where data lives in DuckDB
CLI Overview - Commands that operate on this structure
Quick Start - Create your first project