Open-source Hex alternatives

Redash is an open-source business intelligence and data exploration tool that works entirely in the browser. SQL users connect it to a data source, write queries, turn results into visualizations and share them, so that colleagues without SQL skills can use the results too.

The query editor accepts both SQL and NoSQL queries and offers a schema browser plus auto-complete. Visualizations are built by drag and drop and can be combined into dashboards, and every chart or query has a shareable URL that supports collaboration and peer review. Charts and dashboards can refresh on a schedule, and alerts can notify you when results cross a threshold. Topics mention many sources, including Redshift, BigQuery, Athena, MySQL, PostgreSQL and Databricks.

Redash is written in Python and JavaScript and licensed under BSD-2-Clause. You deploy it on your own infrastructure, commonly with Docker, and it is aimed at analysts and engineers who want a lightweight, query-first dashboard tool rather than a heavy BI suite.

marimo is a reactive Python notebook. Running a cell, or changing a UI element, makes it automatically re-run dependent cells or mark them as stale, so code and outputs stay consistent. Notebooks are saved as pure Python with first-class SQL support, which makes them friendly to git, executable as scripts and deployable as apps. It is released under the Apache-2.0 license.

The project presents itself as a replacement for tools like Jupyter, Streamlit, Jupytext, ipywidgets and Papermill. Highlights include binding sliders, tables and plots to Python without callbacks, querying dataframes, databases and warehouses with SQL, reproducibility with no hidden state and built-in package management, parameterized script execution, sharing as web apps or slides that can run in the browser via WASM, importing functions between notebooks and running pytest on notebooks. The editor has AI assistants and vim keybindings, and works alongside VS Code, Cursor, PyCharm and other editors. A free online notebook is available.

JupyterLab is an extensible environment for interactive and reproducible computing, built on the Jupyter Notebook and its architecture. It is the next-generation user interface for Project Jupyter, offering the familiar building blocks of the classic notebook, such as notebooks, a terminal, a text editor, a file browser and rich outputs, in a more flexible interface.

It can be extended with npm packages that use its public APIs. Prebuilt extensions are distributed through PyPI, conda and other package managers, while source extensions can be installed directly from npm with an extra build step. Extensions can be discovered through the jupyterlab-extension topic on GitHub.

JupyterLab is written in TypeScript, released under the BSD-3-Clause license, and installed with conda, mamba or pip. The project notes that JupyterLab 3 reached end of maintenance in May 2024, with critical fixes backported until the end of that year, and encourages upgrading to JupyterLab 4. Documentation is hosted on ReadTheDocs.

Jupyter Notebook is a web-based notebook environment for interactive computing. Users write code in cells, run it, and see the results next to explanatory text in the same document, which makes it a common choice for data exploration, teaching and documenting analysis.

The project is language-agnostic: it grew out of the IPython notebook, which was split into language-neutral and Python-specific parts in 2015. The current Notebook v7 is built on JupyterLab components for the frontend and the Jupyter Server for the Python backend. Classic Notebook v6 is still maintained for fixes only, and extensions written for older versions are not compatible with v7.

The code is released under the BSD-3-Clause license. The maintainers support the two most recent major versions, Notebook v7 and Classic Notebook v6, and recommend moving off version 5. New features are directed at v7, and contributors are encouraged to build them as Jupyter Server or JupyterLab extensions so they work across Jupyter's interfaces.

Evidence is a code-first business intelligence tool, released as open source, offered in place of drag-and-drop BI products. Reports and dashboards are written as Markdown with SQL queries, which keeps them in version control and lets analysts build interactive data visualizations without a point-and-click builder.

The README calls it agent-ready: you can develop with Evidence's own agent, or locally with coding assistants such as Claude Code or Cursor. Topics mention dbt, DuckDB, Svelte, Tailwind CSS, WebAssembly and self-hosting. A project is created from an installer for macOS, Linux or Windows, and the result can be published on Evidence Studio for hosted reports, or built as a static site and self-hosted on your own infrastructure.

Evidence is written in TypeScript with Svelte and licensed under MIT, and has a Slack community for help. It suits data teams and analysts who are comfortable with SQL and want reports reviewed and deployed like software, rather than assembling dashboards in a GUI.

Apache Zeppelin is a web-based notebook for interactive data analytics. You build data-driven, collaborative documents using SQL, Scala and other languages, and the notebook-style editor supports real-time collaboration between users.

Core features include support for many languages through a pluggable interpreter architecture with process isolation, including Spark, Flink, Python, SQL and Shell and more than 20 interpreters, built-in visualization and dynamic forms, and notebook scheduling with cron. Flexible deployment options are local, Docker, Kubernetes and YARN. Topics place it in the big data ecosystem alongside Spark, Flink, NoSQL and databases.

Zeppelin is an Apache Software Foundation project written in Java and JavaScript and licensed under Apache-2.0. You install it from a binary package or build from source, with user guides, mailing lists and a Jira issue tracker. It suits data engineers and analysts who work on cluster-based data platforms and want shareable, scheduled notebooks.

SQL Explorer is a Django application for SQL reporting. Analysts and developers write queries in a browser-based SQL editor with AI assistance, run them against connected databases, and share the results with teammates. Its tagline stresses simplicity: fast, simple, and free of surprises. The input lists its license only as other.

It supports MySQL, Postgres, Oracle, SQLite, Snowflake, MS SQL Server, and MariaDB, and users can manage connections or upload CSVs and database files. A schema browser with autocomplete, in-browser pivot tables, query history and logs, parameterized queries that generate friendly forms, and keyboard shortcuts are included. Results can be snapshotted on a schedule, emailed when long queries finish, or exposed as JSON endpoints.

The project offers a live demo and documentation, and its website has a pricing and features page, so check there for what is included in free and paid options. It suits teams that want lightweight reporting inside an existing Django project, or a simple tool for sharing queries without adopting a full business intelligence platform.

Shaper is an open-source tool for building data dashboards, reports and customer-facing analytics by writing SQL. It is developed by Taleshape and powered by DuckDB, and it aims to make analytics simple for developers: you write queries, and Shaper turns them into charts and shareable pages.

The data visualization side is SQL-first and designed to work with AI, with a Git-based workflow and the ability to query across data sources. Embedded analytics features include white-labeling and custom styles, row-level security through JWT tokens, and embedding via JavaScript and React SDKs without an iframe. Automated reporting can generate PDF, PNG, CSV and Excel files, send scheduled alerts and reports, and share password-protected links.

Shaper is written in Go and licensed under MPL-2.0. You can try it with a single Docker command or follow the deployment guide for production. Taleshape also offers managed hosting, in its own cloud or in your infrastructure, plus hands-on support, and explains the shared-responsibility model between self-hosting and managed service. It suits product teams adding analytics to their apps.

Preswald is a static-site generator and WASM packager for interactive data apps written in Python. It bundles compute, data access and a user interface into self-contained apps that run locally in the browser, using Pyodide and DuckDB along with libraries such as Pandas, Plotly and Matplotlib.

Apps are written in Python rather than notebooks or JavaScript frameworks, and one command builds a packaged HTML app into a dist folder that can be shared. Built-in components such as tables, charts and forms and a reactive engine based on a dependency graph re-run only what is needed. The project argues it is useful when you want to ship a tool to someone who should not need to install anything, when working with sensitive data that should stay under local control, or when you want AI systems to be able to inspect and modify structured tools.

There is no server, so apps work offline even with large data. Preswald is licensed under Apache-2.0 and is a lightweight alternative to heavier web app platforms for dashboards, reports, prototypes and notebooks.

Briefer combines notebooks and dashboards in a single product, aimed at technical users who need the flexibility of code for analysis while making results easy for non-technical colleagues to view and interact with. It is described by its creators as similar to Notion, but built for code notebooks and dashboards rather than general documents.

Users can create notebooks and dashboards combining Markdown, Python, SQL and native visualizations, build interactive data apps with inputs, dropdowns and date pickers, generate code and queries with an AI that understands the connected database schema, schedule notebooks and dashboards to refresh periodically, and create write-back pipelines for testing changes against data. It also supports real-time multiplayer editing so multiple people can work on the same notebook or dashboard together.

The README states that Briefer has been acquired by Resend, with further updates referenced in the project's blog. It can be run locally by installing it with pip, which requires Docker, or used through a hosted cloud version; it is released under the AGPL-3.0 license and lists PostgreSQL among its supported data sources.