havn 0.2.10

Self-hosted data platform. A Nordic alternative to Databricks/Snowflake. DuckDB + SQL transforms + Python ingest/export. Data in safe waters.

Data in safe waters.
The open-source data platform that runs on your machine. DuckDB + SQL + Python. No cloud required.

Quick Start · Features · Why havn? · Docs · Contributing

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License notice: havn is source-available under the Business Source License 1.1. You can read, run, modify, and use it for any internal or commercial purpose — including in production at your company or at client sites. The one restriction is that you may not offer havn to third parties as a competing hosted or managed service. Each release automatically converts to Apache 2.0 four years after its release date (the current release converts on 2030-04-05). See the License FAQ below for details.

havn (Danish/Norwegian for harbour) is a self-hosted data platform - a Nordic alternative to Databricks and Snowflake for teams that want analytics without the complexity, cost, or data leaving their infrastructure.

Your entire warehouse lives in a single DuckDB file. Transforms are plain SQL. Ingest and export scripts are Python. There's no Jinja, no compilation step, no profiles.yml, and no YAML spaghetti.

git clone https://github.com/chraltro/db.git && cd db && pip install -e . && cd frontend && npm install && npm run build && cd .. && havn init my-project && cd my-project && havn jobs run full-refresh && havn serve

Why havn?

Most data tools force a choice: powerful but complex (Databricks, Snowflake, dbt + Airflow) or simple but limited (CSVs in a folder).

havn gives you the analytical power of a modern data stack in something you can install in one command and run on a laptop.

Pain point	havn's answer
Cloud costs spiraling	Runs locally. DuckDB on your machine. $0/month.
Data leaving your infrastructure	Self-hosted. Your data stays on your hardware. Full stop.
Jinja-templated SQL nobody understands	Plain SQL. Config is a comment. Dependencies are a comment. SQL is just SQL.
30-minute onboarding	30-second onboarding. Install from source and havn init gives you a working pipeline with sample data.
Separate tools for ingest, transform, orchestration, UI	One tool does it all. CLI, web UI, scheduler, connectors - included.
LLMs can't write your DSL	AI-native. Plain SQL + simple conventions = LLMs write correct transforms on the first try.

Features

SQL Transform Engine

Write plain SQL with comment-based config. havn resolves dependencies, builds a DAG, and executes in the right order - with change detection that only rebuilds what changed.

-- config: materialized=table, schema=gold
-- depends_on: silver.customers, silver.orders

SELECT
    c.customer_id,
    c.name,
    COUNT(o.order_id) AS order_count,
    SUM(o.amount)     AS lifetime_value
FROM silver.customers c
LEFT JOIN silver.orders o ON c.customer_id = o.customer_id
GROUP BY 1, 2

Web UI

Full-featured browser interface with Monaco code editor, interactive SQL runner, DAG visualization, data table browser, chart builder, and pipeline monitoring. Dark and light themes included.

havn serve          # http://localhost:3000
havn serve --auth   # with role-based access control

20+ Data Connectors

Connect to Postgres, MySQL, SQLite, Stripe, HubSpot, Google Sheets, S3, REST APIs, and more - from the CLI or the web UI.

havn connect postgres --host localhost --database mydb --user admin
havn connect stripe --api-key sk_live_xxx
havn connect csv --path /data/customers.csv

Notebooks

Interactive .dpnb notebooks with code cells, markdown, and inline results. Use them for exploration, or wire them into your pipeline as ingest/export steps.

Pipeline Orchestration

Define multi-step pipelines in project.yml. Schedule them with cron. Get webhook notifications on completion.

streams:
  daily-refresh:
    schedule: "0 6 * * *"
    steps:
      - ingest: [all]
      - transform: [all]
      - export: [all]
    webhook_url: https://hooks.slack.com/...

Git Integration & CI

Track changes with havn diff, create snapshots with havn snapshot, and generate GitHub Actions workflows with havn ci generate that post data diff comments on PRs.

havn diff                          # what would change?
havn diff --against main           # changes vs a branch
havn snapshot create before-deploy # save state
havn ci generate                   # create GitHub Actions workflow

AI-Native Design

Every project scaffolded with havn init includes LLM context files. Plain SQL + simple conventions means AI assistants write correct code on the first try.

havn context   # generate project summary, paste into any AI chat

Tool	Config file	Auto-included
Claude Code	CLAUDE.md	Yes
Cursor	.cursorrules	Yes
GitHub Copilot	.github/copilot-instructions.md	Yes
Any LLM	havn context	Yes

Quick Start

Install

From PyPI:

pip install havn

From source (for development):

git clone https://github.com/chraltro/db.git
cd db
pip install -e ".[dev]"
cd frontend && npm install && npm run build && cd ..

Create a project

havn init my-project
cd my-project

This scaffolds a complete project with a sample pipeline that fetches earthquake data from the USGS API, transforms it through bronze/silver/gold layers, and exports a report.

Run the pipeline

havn jobs run full-refresh

Explore your data

havn serve                              # open web UI at localhost:3000
havn query "SELECT * FROM gold.earthquake_summary"
havn tables                             # list all tables

Architecture

my-project/
├── ingest/              Python scripts + notebooks that load raw data
│   └── earthquakes.dpnb
├── transform/
│   ├── bronze/          Light cleanup (type casting, dedup)
│   ├── silver/          Business logic (joins, aggregations)
│   └── gold/            Consumption-ready tables
├── export/              Python scripts that push data out
├── notebooks/           Interactive .dpnb notebooks
├── project.yml          Pipelines, connections, schedules
├── .env                 Secrets (never committed)
└── warehouse.duckdb     Your entire database, one file

Data flows through four schemas:

landing/  →  bronze/  →  silver/  →  gold/
 (raw)      (cleaned)   (modeled)   (ready)

The warehouse is a single DuckDB file. Copy it, back it up, version it - it's just a file.

All Commands

Command	Description
havn init <name>	Scaffold a new project
havn jobs run <name>	Run a full pipeline (ingest → transform → export)
havn transform	Build SQL models in dependency order
havn run <script>	Run a single ingest/export script or notebook
havn query "<sql>"	Run ad-hoc SQL queries
havn tables	List warehouse tables and views
havn serve	Start the web UI
havn diff	Preview what would change before running transforms
havn lint	Lint SQL files with SQLFluff
havn history	Show pipeline run log
havn status	Project health: git info, warehouse stats, last run
havn validate	Check project structure, config, and DAG for errors
havn snapshot create	Save a named snapshot of project + data state
havn backup	Back up the warehouse database
havn connect <type>	Set up a data connector
havn watch	Watch files and auto-rebuild on change
havn schedule	Start the cron scheduler
havn checkpoint	Smart git commit with auto-generated messages
havn docs	Generate markdown documentation from warehouse schema
havn context	Generate project summary for AI assistants
havn ci generate	Generate GitHub Actions workflow
havn secrets list/set/delete	Manage .env secrets
havn users create/list/delete	Manage platform users and roles

Comparison

	havn	dbt + Airflow	Databricks	Snowflake
Self-hosted	Yes	Partial	No	No
Setup time	1 min	Hours	Hours	Hours
Monthly cost	$0	$100s+	$1000s+	$1000s+
SQL dialect	Plain SQL	Jinja SQL	Spark SQL	Snowflake SQL
Ingest built-in	Yes	No (need Airbyte etc.)	Yes	Yes
Web UI	Yes	Separate (Airflow UI)	Yes	Yes
Single-file database	Yes	No	No	No
AI-native	Yes	No	Partial	No
Data stays on your machine	Yes	Depends	No	No

havn is the right choice when you want a complete data platform without the infrastructure overhead. It's not trying to replace Snowflake at 10TB scale - it's the best tool for teams working with data that fits on a single machine (which is most teams).

Documentation

• CLAUDE.md - Full technical reference (architecture, conventions, development workflow)
• CONTRIBUTING.md - How to contribute
• havn docs - Auto-generate documentation from your warehouse schema
• havn context - Generate a project summary to paste into any AI assistant

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

# Development setup
git clone https://github.com/chraltro/db.git
cd db
pip install -e ".[dev]"
cd frontend && npm install && npm run build && cd ..
pytest tests/

License

havn is licensed under the Business Source License 1.1. Each release automatically converts to the Apache License 2.0 four years after its release date — the current release converts on 2030-04-05.

BSL 1.1 is a source-available license created by MariaDB and used by projects like HashiCorp Terraform/Vault, Sentry, and CockroachDB. It keeps the full source public while protecting against commercial resale as a competing hosted service.

FAQ

• Can I use havn at my company for free? Yes. Install it, run it, use it in production. There are no restrictions on internal use — no user tiers, no seat counts, no "contact sales".
• Can I modify havn for my own needs? Yes. Fork it, change it, run your modified version internally. The only thing you can't do is sell the modified version as a hosted service.
• Can my consultancy deploy havn at client sites? Yes. Deploying and configuring havn for a client is a service, not hosting. The restriction is on offering havn itself as an ongoing hosted product.
• What exactly is forbidden? Taking havn and offering it to third parties as a paid hosted or managed service that competes with the licensor's commercial offerings.
• When does it become fully open source? Each release converts to Apache 2.0 four years after its release date. The current release converts on 2030-04-05.
• Can I contribute? Yes — see CONTRIBUTING.md. Contributions will require a Contributor License Agreement so they can be included in both the BSL core and any future commercial distribution.