getdango 0.0.1

Local-first data platform - Laptop to cloud data stack in one weekend

🍡 Dango

Production-ready analytics platform in minutes, not weeks

Dango deploys a complete data stack (DuckDB + dbt + Metabase) to your laptop with one command.

Status

🚀 MVP Development - 75% Complete | Target Release: November 29, 2025

• ✅ Implemented: CLI (9 commands), 29 data sources, Web UI, dbt auto-generation
• ⏳ Remaining: Auto-triggers, demo project, bootstrap script, PyPI packaging
• 📅 Timeline: 4 weeks to v0.1.0 release (see TIMELINE.md)

Quick Start (Coming Soon)

# One-line install
curl -sSL getdango.dev/bootstrap | bash -s my-analytics

# Interactive setup
cd my-analytics
dango init .

# Add data sources
dango source add  # Interactive wizard

# Start platform
dango start

# Load data
dango sync

# Open dashboards
open http://my-analytics.dango

Features

✅ Implemented

• CLI Framework: 9 commands (init, start, stop, status, info, config, source, sync, dashboard)
• Data Sources: 29 sources integrated (27 dlt verified sources + CSV + REST API)
• Web UI: FastAPI backend with live pipeline monitoring
• dbt Auto-Generation: Automatically generate staging models from source schemas
• Metabase Dashboards: API-based dashboard provisioning
• Incremental Loading: CSV with metadata tracking and 4 dedup strategies
• Config Validation: Pydantic-based schema validation with friendly errors

🚧 Coming in v0.1.0 (MVP Release: Nov 29)

• Network Architecture: <project>.dango domains with shared port 80
• Auto-Triggers: File watcher with 10-minute debounce → auto-sync → auto-dbt
• Demo Project: Sample data + pre-built dashboards (dango demo create)
• Dashboard Persistence: Export/import workflow for git
• Metabase Auto-Setup: Zero-config with auto-login and organization branding
• dbt Modeling Wizard: Template-based fact/dimension table creation
• OAuth Helpers: Guided flows for Facebook, Google
• Bootstrap Install: One-command setup via curl
• PyPI Package: Install via pip install dango-data

📋 Post-MVP (v0.2+)

• Orchestration with Prefect Cloud
• Cloud deployment options (Railway, Render, DigitalOcean)
• Advanced monitoring and alerting
• Multi-user collaboration features

Architecture

Data Layers:

• raw - Immutable source of truth (with metadata)
• staging - Clean, deduplicated data
• intermediate - Reusable business logic
• marts - Final business metrics

Tech Stack:

• DuckDB - Analytics database (embedded, fast)
• dbt - SQL transformations
• dlt - API integrations (29 sources: 27 verified + CSV + REST)
• Metabase - BI dashboards
• Docker - Service orchestration
• FastAPI - Web UI backend
• nginx - Reverse proxy with domain routing

Target Users

• Solo data professionals
• Fractional consultants
• SMEs needing analytics fast
• Anyone who wants a "real" data stack without the complexity

Why Dango?

Most tools force you to choose:

• ❌ Local-first (limited features) OR Cloud (expensive, complex)
• ❌ No-code (inflexible) OR Full-code (steep learning curve)
• ❌ Fast setup (toy project) OR Production-grade (weeks of work)

Dango gives you both:

• ✅ Local-first AND production-ready
• ✅ Wizard-driven AND fully customizable
• ✅ Fast setup AND best practices built-in

Development

# Clone repo
git clone https://github.com/getdango/dango
cd dango

# Install in development mode
pip install -e ".[dev]"

# Run CLI
dango --help

# Run tests (coming soon)
pytest

Documentation

• MVP Roadmap - Complete MVP specifications and timeline
• Timeline - Visual 4-week roadmap to v0.1.0 release
• Implementation Progress - Development log
• Architecture - System design overview
• CSV Loading Design - Phase 1 decisions

Contributing

We're in active MVP development! Contributions welcome after v0.1.0 releases (Nov 29, 2025).

See CONTRIBUTING.md (coming soon) for guidelines.

License

Apache 2.0 - See LICENSE for details.

Links

• Homepage: https://getdango.dev (coming soon)
• Docs: https://docs.getdango.dev (coming soon)
• GitHub: https://github.com/getdango/dango
• Issues: https://github.com/getdango/dango/issues

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