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What is Dango?

Dango is an open-source data platform that integrates production-grade tools (dlt, dbt, DuckDB, Metabase) into a single, cohesive platform.

Works on your laptop today. Designed to scale to production tomorrow.

The Problem

Building a data platform typically requires:

  • Weeks of setup and configuration
  • Deep knowledge of multiple tools
  • Complex infrastructure decisions
  • Choosing between simple (limited) or complex (powerful) tools

Result: Data teams spend more time on infrastructure than analysis.

The Solution

Dango gives you a complete data stack with one command:

dango init

You get:

  • dlt for data ingestion (29+ verified sources)
  • dbt for SQL transformations
  • DuckDB as your analytics database
  • Metabase for dashboards and SQL queries
  • Web UI for monitoring and management

Architecture

Dango uses a layered data architecture:

graph LR
    A[Data Sources] --> B[dlt]
    B --> C[Raw Layer]
    C --> D[dbt]
    D --> E[Staging]
    E --> F[Intermediate]
    F --> G[Marts]
    G --> H[Metabase]

Data Layers

  1. Raw - Immutable source of truth with metadata
  2. Staging - Clean, deduplicated data
  3. Intermediate - Reusable business logic
  4. Marts - Final business metrics

Tech Stack

Component Purpose Why This Tool?
DuckDB Analytics database Embedded, fast, no server needed
dlt Data ingestion 29+ sources, schema evolution
dbt Transformations SQL-based, version controlled
Metabase BI dashboards Auto-configured, easy to use
Docker Service orchestration Consistent environments
FastAPI Web UI backend Fast, modern Python

Core Features

Data Ingestion

  • 29+ verified dlt sources (Stripe, Google Sheets, GA4, Facebook Ads, etc.)
  • CSV upload and auto-sync
  • Custom source development
  • OAuth authentication for cloud sources

Transformations

  • dbt auto-generation for staging models
  • Full dbt project access
  • SQL-based transformations
  • Incremental model support

Monitoring

  • Web UI with live pipeline status
  • File watcher with auto-triggers
  • Token expiry warnings
  • Validation and health checks

Dashboards

  • Metabase auto-configured with DuckDB
  • Pre-built dashboard templates
  • SQL query interface
  • Dashboard backup and restore

Current Status

Dango is currently in early development (MVP). Core functionality is stable and usable.

What Works Now

  • ✅ Full CLI with 10+ commands
  • ✅ CSV, Stripe, Google Sheets, GA4, Facebook Ads sources
  • ✅ dbt auto-generation for staging models
  • ✅ Web UI with live monitoring
  • ✅ Metabase dashboards
  • ✅ File watcher with auto-triggers
  • ✅ Custom sources via dlt_native type

Coming Soon

  • 🚧 Additional data sources (Google Ads), demo project, expanded documentation
  • 🔮 Cloud deployment guides, advanced scheduling, team collaboration

Design Philosophy

Dango is built on two core principles:

Opinionated but Modular

Best practices are built-in so you can focus on insights, not infrastructure. As the open-source data ecosystem evolves, components can be swapped for better alternatives without rebuilding your entire stack.

Democratize Analytics Infrastructure

Enterprise-grade data tooling shouldn't require a dedicated platform team. Dango brings production-quality patterns to teams of any size—the same tools used by sophisticated data teams, packaged for accessibility.

Target Users

  • Solo data professionals - Complete stack, zero complexity
  • Small data teams - Full analytics stack that grows with you
  • Fractional consultants - Fast client onboarding
  • SMEs - Analytics infrastructure without the overhead
  • Learners - Production tools without production costs

Why Dango vs. Alternatives?

vs. Cloud Platforms (Snowflake, BigQuery)

Aspect Dango Cloud Platforms
Setup One command, ready in minutes Assemble and integrate multiple tools
Cost Free and open source Pay for compute and storage
Stack Integrated (dlt + dbt + DuckDB + Metabase) Build your own toolchain
Iteration Instant local feedback loop Round-trip to cloud for each change
Scale Local compute limits Scales to petabytes

vs. Managed ETL Tools (Fivetran, Airbyte Cloud)

Aspect Dango Managed ETL Tools
Customization Fully customizable Limited to supported connectors
Configuration Version controlled (YAML, SQL) UI-based, harder to track changes
Cost Free and open source Subscription or usage-based pricing
Integration Complete stack included ETL only—BI, transforms, warehouse separate
Complexity Requires some code for advanced use Point-and-click for supported sources

Note

Some tools like Airbyte have open-source versions, but require separate setup for orchestration, transformations, and BI.

vs. DIY Stack

Dango DIY Stack
✅ Integrated from day one ❌ Weeks of integration work
✅ Best practices built-in ⚠️ Easy to make mistakes
✅ Maintained by community ❌ You maintain everything
⚠️ Opinionated structure ✅ Complete flexibility

What Dango is NOT

  • Not a SaaS platform - It's a CLI tool that runs locally
  • Not cloud-only - MVP is local, cloud support coming later
  • Not a BI tool - It integrates BI (Metabase) but focuses on data infrastructure
  • Not production-ready for enterprise - Currently in early development (MVP)

Next Steps

Ready to try Dango?

  1. Install Dango - Get set up in minutes
  2. Quick Start - Run your first pipeline
  3. Core Concepts - Deep dive into architecture

Questions?