Exploring Project Jupyter, Deepnote & Visivo

Project Jupyter vs. Deepnote vs. Visivo

Compare key features, capabilities, and differentiators between Project Jupyter, Deepnote, 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	Project Jupyter	Deepnote	Visivo
Deployment Model	Open-source (local), Jupyter server, JupyterHub deployment	Cloud (browser-based), Enterprise deployment, Private cloud	Open-source, Cloud Service, Self-hosted
Pricing	Free (BSD license)	Free tier (limited projects); paid for premium features. Closed-source.	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.

Project Jupyter

Data scientistsResearchersEngineers

Deepnote

Data science teamsEducatorsAnalysts collaborating on notebooks

Visivo

Analytics EngineersData teamsBusiness usersEngineers

Ease of Development & Deployment

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

Project Jupyter

2/5

Deepnote

4/5

Visivo

5/5

Key Integrations & Ecosystem

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

Project Jupyter

Python/Julia/R librariesSQL connectorsCustom API integrations

Deepnote

SQL databases (PostgreSQL, BigQuery, etc.)Cloud storage (Google Drive, S3)dbt workflow integration

Visivo

dbt coreAll major databasesCustom connector frameworkSlack for alertsGithub

Visualization Capabilities

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

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).

Deepnote

Jupyter-like notebooks with collaborative editing. Supports interactive visualizations by writing code (Python, R, SQL blocks). Has a GUI for basic charts: you can switch a SQL cell's results into a chart view (bar/line) quickly. Can arrange outputs into a dashboard layout for sharing. Custom viz requires coding (e.g., Plotly, seaborn).

Visivo

Highly custom UI with easy defaults

Detailed Differentiators

Each platform's unique strengths and limitations.

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).

Deepnote

Real-time collaboration on notebooks (like Google Docs for Jupyter) – multiple users can work simultaneously. Great for mixed code-and-text narratives and then turning into lightweight dashboards for stakeholders.

Still requires coding for most analysis; not a drop-in replacement for tools like Tableau for a pure business user.

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.

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.

Deepnote

DB Access: Yes, uses direct credentials to query databases in SQL cells. Virtualization: No separate layer – it's a client executing queries or code. Push: No (though you could push data via Python in a notebook to an external system). Other: Deepnote runs in cloud with project-specific isolation; offers Google SSO. Not designed for role-based consumption – notebooks can be shared via link with view/edit rights.

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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