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Lunapad is an open-source data notebook for business intelligence, data analysis, reporting, and dbt workflows. It lets users explore data, ask questions, build reusable analysis steps, and publish reports from a single browser-based workspace. Each notebook cell can be written in SQL, PRQL, Python, or a visual pipeline, allowing both technical and non-technical users to work with data in a flexible format.
Lunapad supports CSV, Parquet, JSON, spreadsheets, DuckDB, Postgres, MySQL, ClickHouse, Snowflake, BigQuery, MongoDB, Cassandra, Elasticsearch, and other data sources through Trino. Users can combine data from multiple sources in one query, run Python with libraries such as pandas, NumPy, and Plotly, and view results inline. Named steps create a dependency graph that helps Lunapad rerun only affected queries, charts, and report components when data changes.
The platform also supports interactive report publishing, scheduled refreshes in hosted workspaces, Excel exports, dbt project integration, data lineage, and promotion of stable notebook logic into dbt models. Lunapad runs DuckDB in the browser by default, keeping local files on the user’s computer unless shared data connections are configured. It can also be self-hosted with Docker Compose for teams that need a shared data analytics and reporting environment.
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