Exploring Mode, Project Jupyter & Visivo

Mode Vs. Project Jupyter Vs. Visivo

In this article, we'll compare the key features, capabilities, and differentiators between Mode, Project Jupyter, Visivo. This comprehensive comparison will help you make an informed decision about which platform best suits your data visualization and analytics needs.

Quick Comparison

A high-level overview of key features and capabilities across these BI tools. This comparison helps you quickly identify which platform best matches your needs.

Feature	Mode	Project Jupyter	Visivo
Deployment Model	Cloud (SaaS), Enterprise deployment, Private cloud	Open-source (local), Jupyter server, JupyterHub deployment	Open-source, Cloud Service, Self-hosted
Pricing	Subscription per user (tiered features). Proprietary.	Free (BSD license)	Open source (GPL-3.0)
Cost	$$$$	$	$
Git Integration	❌	❌	✔️
CI/CD & Testing	❌	❌	✔️
Real-time	❌	❌	❌
AI	❌	❌	✔️
Visual to Code	❌	❌	✔️
DAG-Based	❌	❌	✔️

Deployment & Pricing

Understanding the deployment options and pricing structure is crucial for making an informed decision. Here's how each platform handles deployment and what you can expect in terms of costs.

Tool	Deployment Model	Pricing	Cost
Mode	Cloud (SaaS), Enterprise deployment, Private cloud	Subscription per user (tiered features). Proprietary.	$$$$
Project Jupyter	Open-source (local), Jupyter server, JupyterHub deployment	Free (BSD license)	$
Visivo	Open-source, Cloud Service, Self-hosted	Open source (GPL-3.0)	$

Target Users & Use-Cases

Each BI tool is designed with specific user personas in mind. Understanding the target audience helps ensure you choose a platform that aligns with your team's skills and needs.

Mode

Data analystsData scientistsBusiness teams consuming insights

Project Jupyter

Data scientistsResearchersEngineers

Visivo

Analytics EngineersData teamsBusiness usersEngineers

Ease of Development & Deployment

The development experience can significantly impact your team's productivity. This section compares how easy it is to build, deploy, and maintain dashboards in each platform.

Mode

Project Jupyter

Visivo

Key Integrations & Ecosystem

A robust ecosystem of integrations is essential for modern BI tools. Here's how each platform connects with other tools in your data stack.

Mode

Multiple SQL databasesPython/R for advanced analyticsAPI and webhooks for automation

Project Jupyter

Python/Julia/R librariesSQL connectorsCustom API integrations

Visivo

dbt coreAll major databasesCustom connector frameworkSlack for alertsGithub

AI & Advanced Features

Artificial intelligence is transforming how we interact with data. Compare the AI capabilities and advanced features offered by each platform.

Tool	AI Features
Mode	❌
Project Jupyter	❌
Visivo	✔️

Visualization Capabilities

The ability to create compelling and insightful visualizations is a key differentiator between BI tools. Here's how each platform handles data visualization.

Mode

Hybrid analytics: Start with SQL query, then seamlessly use results in a Python or R notebook within Mode. Visualizations: either use the built-in chart builder on query results (which is simple but covers basics), or output custom plots from the notebook (Matplotlib, etc.). You can combine these in a report. Good for analytical narratives.

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 has its own strengths and weaknesses. Here's a detailed breakdown of what sets each tool apart, including both advantages and limitations.

Mode

+ Combines analytics workflows: SQL + Python/R in one tool, enabling advanced analysis (statistical, ML) with sharing in one place. Great collaboration – team commentary and shareable, embeddable reports.

− Not aimed at strict self-service for non-analysts (business users usually consume results, not build). Lacks a governed semantic layer – relies on analysts to maintain consistency.

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

Security and architecture are critical considerations for enterprise deployments. Here's how each platform handles data security and system architecture.

Mode

DB Access: Yes, Mode executes queries against your DB whenever a report refreshes. Virtualization: No separate layer – it's basically a client querying the DB. Push: No, except you can schedule exports of results to external systems. Other: Row-level security must be implemented in SQL (no built-in feature for it). Supports SSO and fine-grained access to reports (who can view/edit).

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 that make it an excellent choice for modern data teams.

DAG-Based Architecture: Enables complex data transformations and dependencies
Visual to Human-readable Code: Seamlessly switch between visual and code-based development
Ease of Development: Multiple approaches to build for both technical and non-technical users
AI-Powered Development: Leverage AI to accelerate dashboard creation
Git Integration: Full version control and collaboration capabilities

Ready to experience the power of modern BI? Try Visivo today and see how it compares to other tools in your stack.

$pip install visivo