Exploring Preset, Project Jupyter & Visivo

Preset vs. Project Jupyter vs. Visivo

Compare key features, capabilities, and differentiators between Preset, 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	Preset	Project Jupyter	Visivo
Deployment Model	Cloud (multi-tenant), Cloud (VPC), Managed service	Open-source (local), Jupyter server, JupyterHub deployment	Open-source, Cloud Service, Self-hosted
Pricing	Managed service (subscription per creator & usage). Underlying Superset is Apache-licensed.	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.

Preset

Data teams wanting managed SupersetOrganizations without resources to self-hostEnterprise analytics teams

Project Jupyter

Data scientistsResearchersEngineers

Visivo

Analytics EngineersData teamsBusiness usersEngineers

Ease of Development & Deployment

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

Preset

4/5

Project Jupyter

2/5

Visivo

5/5

Key Integrations & Ecosystem

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

Preset

Same database integrations as Supersetdbt Cloud for metadataOAuth connections to popular DBs

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.

Preset

Identical visualization capabilities to Apache Superset (Preset is built on Superset) – plus a nicer UI/UX and theme. Custom visualizations can be added via Preset's marketplace.

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.

Preset

Easiest way to use Superset – fully managed infrastructure and support from Superset experts. New features and custom connectors often available.

Still catching up to feature parity with mature BI tools in terms of collaboration (Git, fine-grained content permissions).

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.

Preset

DB Access: Yes (your databases' credentials are stored in Preset; it queries them directly). Virtualization: No, live queries on sources (with optional result caching). Push: No – you supply data to your DB, Preset pulls on viz. Other: Managed security – SSO integration, teams/roles setup in UI. Data stays in your cloud DB; Preset does not persist data (aside from cached query results).

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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