Open-source Reveal alternatives

Apache Superset is a data exploration and visualization platform that runs as a web application. Analysts and business users can connect it to a database, build charts without code, assemble them into dashboards and share them, so it can augment or replace proprietary business intelligence tools for many teams.

The no-code chart builder sits next to a web-based SQL editor for advanced queries, and a lightweight semantic layer lets teams define reusable dimensions and metrics. Visualizations range from simple bar charts to geospatial views. Superset can query any SQL datastore or engine provided a matching Python database driver and SQLAlchemy dialect exist, including Presto, Trino and Athena. A configurable caching layer reduces database load, and security roles and authentication options are extensible.

The stack is Python with Flask on the back end and React on the front end, under the Apache-2.0 license. A REST API and extension framework allow programmatic control and customization. Documentation is split into guides for users, administrators and developers, and it is a typical choice for organizations that want a self-managed BI layer.

Metabase is an open-source business intelligence and embedded analytics tool meant for everyone in a company, not only analysts. People can ask questions about their data without knowing SQL, while an SQL editor is available for more complex queries. Written in Clojure, it connects to databases such as PostgreSQL and MySQL.

Its features include interactive dashboards with filters, auto-refresh and custom click behavior, plus documents for long-form analysis that colleagues can comment on. An AI assistant called Metabot helps answer questions and write queries, and you can build your own AI agent against your data. Data Studio supports transforming raw data into analytics-ready tables and defining canonical metrics. Alerts and scheduled dashboard subscriptions go to email, Slack or a webhook, and a Library plus Git integration help version your work.

Metabase can also be embedded in your own product with components for charts, dashboards, a data browser and AI chat, and granular permissions work for internal teams and embedded customers. You can self-host it or use Metabase Cloud, which the project says includes support, backups, upgrades and a free trial. The repository license is listed as 'Other', so review its terms.

Chartbrew is an open-source web application that connects directly to databases and APIs and uses the data to build charts and dashboards. Its tools include a chart builder, editable dashboards, embeddable charts, a query and request editor and team features for sharing reports.

The platform also includes an AI assistant and scheduling, and its topics reference MySQL, PostgreSQL, MongoDB, Firestore and Firebase as data sources, with the latest list of supported sources on the project website. To self-host, the README lists Node.js 22 or newer, MySQL 5 or PostgreSQL 12.5 or newer, and Redis 6 or newer as prerequisites. The front end uses React and the back end Node.js.

A hosted Chartbrew service is available, and the repository license is listed as 'Other' on GitHub, so check the terms for your use. Chartbrew suits agencies, startups and teams who want shareable client dashboards without building a BI stack.

Graphic Walker, from Kanaries, is an open-source visual analytics tool positioned as an alternative to Tableau. Users build charts and pivot tables by dragging fields onto visual channels, and can also ask questions in natural language. It is released under the Apache-2.0 license.

Its main distinction is that it ships as a React component that you embed in your own application, rather than as a heavy business-intelligence platform. The interface follows a grammar of graphics based on Vega-Lite, supports spatial visualizations from GeoJSON and TopoJSON, and includes light and dark themes. A data explainer suggests why patterns occur, and a hand-writable chart specification called TerseSpec describes charts in compact form.

Computation can run in the browser using web workers, which allows a purely front-end setup, or queries can be passed to your own computation service, for example one using DuckDB as in the Python companion PyGWalker. It suits developers who want to add exploratory analysis to a product and data scientists who want fast visual exploration without a full BI stack.