What is Dango?¶

Dango is a complete, open-source data platform — ingestion, transformation, a warehouse, and dashboards, built on production-grade tools (dlt, dbt, DuckDB, Metabase).

Develop locally. Deploy to the cloud when you're ready.

The Problem¶

dlt, dbt, DuckDB, and Metabase are each excellent on their own. The friction is running them together as one system:

• No shared authentication, scheduling, or access control between them out of the box
• Nobody owns credential health, backups, or catching a schema change before it breaks a dashboard
• Getting from "four working tools" to "a platform a team can actually operate" is the part that takes weeks — and most DIY setups never build the operational layer at all

The Solution¶

Dango is that operational layer, already built in — not a one-time setup, but a platform maintained through updates as it runs:

dango init

You get:

• dlt for data ingestion (35 data sources)
• dbt for SQL transformations
• DuckDB as your analytics database
• Metabase for dashboards and SQL queries
• Web UI for monitoring, management, and authentication
• Scheduling, credential health checks, and schema drift detection running without configuration
• 50+ CLI commands for every aspect of your data workflow

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

Learn more in Data Layers.

Tech Stack¶

Core Features¶

Data Ingestion¶

• 35 data sources (Stripe, Google Sheets, GA4, Facebook Ads, Salesforce, HubSpot, and more)
• File import for CSV, JSON, and Parquet files
• Custom source development via dlt_native and REST API types
• OAuth authentication for cloud sources

Learn more in Data Sources.

Transformations¶

• dbt auto-generation for staging models
• Full dbt project access with custom models
• SQL-based transformations
• Incremental model support
• Branch-based development with dango dev

Learn more in Transformations.

Monitoring & Scheduling¶

• Web UI with live pipeline status and sync history
• Scheduled syncs with flexible cron expressions
• Schema drift detection with automatic alerts
• Webhook notifications (Slack, email, custom endpoints)
• Health monitoring with capacity tracking
• Token expiry warnings for OAuth sources

Learn more in Scheduling & Monitoring.

Authentication & Security¶

• Authentication enabled by default — configured automatically during dango init
• Session-based auth with configurable timeouts
• Admin password management via CLI and Web UI
• Metabase SSO bridge — access dashboards through the Web UI without a separate login
• Credential encryption for source secrets
• Audit logging for security events

Learn more in Security.

Governance¶

• Automated PII scanning across your data warehouse
• Column-level descriptions synced to Metabase
• Data catalog with schema documentation
• dbt test integration for data quality monitoring

Learn more in Data Catalog and PII Scanning.

Notebooks¶

• Marimo notebook integration for interactive data exploration
• Read-only DuckDB snapshots to avoid write locks
• Built-in templates for common analysis patterns

Learn more in Notebooks.

Dashboards¶

• Metabase auto-configured with DuckDB
• Pre-built pipeline health dashboard (dango dashboard provision)
• SQL query interface
• Dashboard backup and restore (dango metabase save / dango metabase load)

Learn more in Dashboards.

Cloud Deployment¶

• One-command deployment to any server via SSH
• DigitalOcean provisioning with dango deploy
• Bring Your Own Server (BYOS) support
• Automatic TLS via Caddy, fail2ban, unattended upgrades
• Push-based deployment model with dango remote push

Learn more in Deployment.

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)¶

vs. Managed ETL Tools (Fivetran, Airbyte Cloud)¶

vs. DIY Stack¶

What Dango is NOT¶

• Not a SaaS platform — It's a CLI tool you run locally or on your own server
• Not cloud-only — Develop locally first, deploy to the cloud when you're ready
• Not a BI tool — It integrates BI (Metabase) but focuses on data infrastructure
• Not a petabyte-scale warehouse — Designed for small-to-medium datasets on DuckDB

Next Steps¶

Ready to try Dango?

1. Install Dango — Get set up in minutes
2. Quick Start — Run your first pipeline
3. Your First Dashboard — Build a Metabase dashboard
4. Core Concepts — Deep dive into architecture

Questions?¶

• GitHub: github.com/getdango/dango
• Issues: github.com/getdango/dango/issues
• PyPI: pypi.org/project/getdango