Grist

Grist is an open-source, self-hosted relational spreadsheet. It combines the flexible interface of a spreadsheet with the structure of a database, where columns hold a single data type and can be populated by formulas. The software is available as a server-based web app via grist-core, a desktop application for local files, and a static browser-based build for website integration.

Users deploy Grist to organize data, build internal tools, and create custom dashboards. It supports various deployment methods, including Docker, and allows for local file management through a SQLite-based format. This architecture ensures that data remains portable and can be read by any tool compatible with SQLite, facilitating easy backups and migrations between different hosts.

Key features

• Python-based formulas with support for the standard library and an AI assistant
• Drag-and-drop dashboards featuring charts, calendar widgets, and summary tables
• Relational data capabilities including two-way references and reference lists
• Native form builder with support for file attachments and URL pre-population
• Incremental CSV imports to prevent data duplication during updates
• REST API with outgoing webhooks and Zapier integration
• Granular access control for individual rows, columns, and tables
• Real-time collaboration with cell comments and suggested changes
• Sandboxing options using gVisor, native macOS sandboxing, or Wasm

Grist is designed for users who need more structure than a traditional spreadsheet but prefer a visual interface over raw SQL. It integrates with Google Drive and supports OIDC and SAML for single sign-on. For high-security environments, it offers document-level sandboxing to handle untrusted files safely. The platform also includes specialized editors for dates, times, and currency, along with conditional formatting to highlight important information based on specific formulas.

It is a hybrid tool for data management, bridging the gap between flat files and relational databases for efficient organization and analysis.