Exploring Evidence.dev, Project Jupyter & Visivo

Evidence.dev vs. Project Jupyter vs. Visivo

Compare key features, capabilities, and differentiators between Evidence.dev, Project Jupyter, Visivo. This comprehensive analysis will help you make an informed decision for your data visualization needs.

Quick Comparison

Key features and capabilities at a glance

Feature	Evidence.dev	Project Jupyter	Visivo
Deployment Model	Self-host (static site), Self-host (server), Vercel/Netlify deployment	Open-source (local), Jupyter server, JupyterHub deployment	Open-source, Cloud Service, Self-hosted
Pricing	Open-source (MIT); free to use. New hosted service as of fall 2024 (can also deploy on Vercel, Netlify, etc.)	Free (BSD license)	Open source (GPL-3.0)
Cost	$	$	$
Git Integration
CI/CD & Testing
Real-time
AI Features
Visual to Code
DAG-Based

Target Users & Use-Cases

Each BI tool is designed with specific user personas in mind.

Evidence.dev

Analytics engineersData-savvy users who prefer code/markdown workflowEngineers & Academics

Project Jupyter

Data scientistsResearchersEngineers

Visivo

Analytics EngineersData teamsBusiness usersEngineers

Ease of Development & Deployment

Development experience directly impacts team productivity and time-to-value.

Evidence.dev

3/5

Project Jupyter

2/5

Visivo

5/5

Key Integrations & Ecosystem

A robust ecosystem of integrations is essential for modern BI tools.

Evidence.dev

SQL databases via JDBC/ODBCdbt metadata integrationStatic site deployment platforms

Project Jupyter

Python/Julia/R librariesSQL connectorsCustom API integrations

Visivo

dbt coreAll major databasesCustom connector frameworkSlack for alertsGithub

Visualization Capabilities

The ability to create compelling visualizations is key to data storytelling.

Evidence.dev

Reports built as Markdown with embedded SQL and charts. Outputs static HTML dashboards. Customization via editing Markdown/HTML/CSS; not a point-and-click UI.

Project Jupyter

Not a conventional BI tool – it's a computing environment. Visuals come from libraries (Matplotlib, Plotly, etc.) within code cells. Highly flexible outputs (any HTML/JS). Sharing typically static (not interactive unless using Voila or similar to create dashboards).

Visivo

Highly custom UI with easy defaults

Detailed Differentiators

Each platform's unique strengths and limitations.

Evidence.dev

+ Write dashboards as code – lightweight, reproducible, easily integrated with dbt pipelines. Great for data narratives that combine text, data, and charts.

Static output means no ad-hoc drilling by end users; requires comfort with writing Markdown/SQL.

Project Jupyter

Extreme flexibility – you can do anything in code. Huge ecosystem of libraries for analysis and visualization.

Not user-friendly for non-coders; to share insights, often notebook is converted to PDF/HTML which is static. Multi-user collaboration and security are not provided out-of-the-box (need JupyterHub or similar).

Visivo

BI-as-code approach enables version control, collaboration, and CI/CD workflows. DAG-based architecture provides powerful data transformation capabilities and dependency management. Seamless visual-to-code workflow allows both technical and non-technical users to build dashboards effectively.

Requires understanding of data concepts; not a pure drag-and-drop tool like Tableau. Initial setup requires technical knowledge for optimal configuration.

Security & Architecture

Critical considerations for enterprise deployments.

Evidence.dev

DB Access: Queries run at build time (or page load if in server mode); not needed for end viewer (static pages). Virtualization: By nature, it materializes results into the page (no live DB query once published). Push: Yes – essentially a push of data into static site. Other: No runtime user management (pages are static); security depends on where you host (you wouldn't include sensitive data in a public build).

Project Jupyter

DB Access: If a notebook connects to a DB, it does so directly (with credentials in code or config). Virtualization: No – but you could use tools like Trino via Python to virtualize in code. Push: No, unless custom code to push data. Other: Jupyter itself has no auth (except if behind JupyterHub). Security concerns if sharing notebooks with sensitive data output.

Visivo

No db access required. Very strong security features due to the DAG-based access controls and the push based deployment model.

Why Visivo Stands Out

While each platform has its strengths, Visivo offers unique advantages for modern data teams.

DAG-Based Architecture for complex data transformations

Visual to Human-readable Code conversion

Multiple development approaches for all skill levels

AI-Powered dashboard creation

Full Git integration and version control

Open-source with enterprise features

Ready to Experience Modern BI?

Try Visivo today and see how it transforms your data analytics workflow.

$ curl -fsSL https://visivo.sh | bash

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