Lunapad guide
Write SQL, PRQL, Python, or visual steps; connect files and databases; publish browser reports; and move trusted work into dbt. This guide covers Lunapad from first run to self-hosting in production.
Start Lunapad, open the hosted or self-hosted data notebook, sign in, and explore sample data.
Learn how Lunapad notebooks, named cells, dependencies, and reusable analysis steps work.
Write SQL, PRQL, Python, and visual query-builder steps inside an open-source data notebook.
Connect files, spreadsheets, DuckDB, Postgres, ClickHouse, MySQL, Snowflake, and Trino sources.
Turn notebook results into tables, profiling views, charts, dashboards, and shareable reports.
Use Markdoc widgets to build filterable BI reports from Lunapad notebook results.
Draft queries with AI while keeping control of hosted, self-hosted, or local model settings.
Use Lunapad with dbt projects, inspect lineage, and promote notebook analysis into models.
Publish Lunapad analyses as browser reports with public links, filters, and Evidence.dev output.
Automate Lunapad notebooks and connect MCP clients for scripted data exploration workflows.
Self-host Lunapad with Docker Compose, environment variables, team settings, and backups.
Fix common Lunapad setup, data connection, notebook, report, and self-hosting issues.
Review BI notebooks with comment threads, mentions, inbox notifications, and team workflows.