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Guides & Tutorials

Practical, end-to-end tutorials for real-world analytics use cases.

Overview

These guides walk through complete workflows using Dango. Each tutorial is designed to be followed from start to finish, demonstrating realistic data scenarios.

Available Tutorials

  • E-commerce Analytics

    Build a complete e-commerce analytics pipeline with CSV orders and Stripe payments.

    Duration: ~30 minutes

    Start Tutorial

  • Financial Reporting

    Create financial dashboards with Stripe revenue data and dbt transformations.

    Duration: ~20 minutes

    Start Tutorial

  • Multi-Source Integration

    Combine data from multiple sources into unified analytics.

    Duration: ~25 minutes

    Start Tutorial

  • Custom API Integration

    Build a custom data source for any REST API.

    Duration: ~30 minutes

    Start Tutorial

Prerequisites

Before starting any tutorial, ensure you have:

  • Dango installed (pip install getdango)
  • A Dango project initialized (dango init)
  • Docker running (for Metabase)
  • Basic familiarity with SQL

Tutorial Approach

Each guide follows this structure:

  1. Setup - Create project and configure sources
  2. Ingest - Sync data into DuckDB
  3. Transform - Build dbt models
  4. Visualize - Create dashboards in Metabase
  5. Iterate - Add more sources or transformations
graph LR
    A[Quick Start] --> B[E-commerce Tutorial]
    B --> C[Financial Reporting]
    B --> D[Multi-Source]
    D --> E[Custom API]
Experience Start With
New to Dango Quick Start → E-commerce Tutorial
Know the basics Financial Reporting or Multi-Source
Advanced user Custom API Integration

Sample Data

Some tutorials use sample data. Download links are provided in each tutorial, or use your own data for a more realistic experience.

Use Your Own Data

These tutorials work best with real data. If you have access to a Stripe test account or can export your own CSVs, use those instead of sample files.

Getting Help

If you get stuck:

  1. Check the Troubleshooting Guide
  2. Review the relevant section docs (Data Sources, Transformations, etc.)
  3. Ask in GitHub Discussions