Exploring Holistics, Project Jupyter & Visivo

Holistics Vs. Project Jupyter Vs. Visivo

In this article, we'll compare the key features, capabilities, and differentiators between Holistics, 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	Holistics	Project Jupyter	Visivo
Deployment Model	Cloud SaaS (Holistics 4), On-prem enterprise, Private cloud	Open-source (local), Jupyter server, JupyterHub deployment	Open-source, Cloud Service, Self-hosted
Pricing	Commercial (proprietary). Free trial available.	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
Holistics	Cloud SaaS (Holistics 4), On-prem enterprise, Private cloud	Commercial (proprietary). Free trial available.	$$$$
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.

Holistics

Data teams (analytics engineers)Business users exploring defined dataData-driven organizations

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.

Holistics

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.

Holistics

SQL databases (Snowflake, BigQuery, PostgreSQL, etc.). dbt integration: Yes – can import dbt models and exposures, aligning Holistics model with dbt transforms.SQL databases (SnowflakeBigQueryPostgreSQLetc.). dbt integration: Yes – can import dbt models and exposuresaligning Holistics model with dbt transforms.

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
Holistics	❌
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.

Holistics

Combines a code-based 'data modeling layer' with a drag/drop UI for end users. Data team defines datasets, dimensions, measures in YAML (or UI), then business users create charts by selecting those fields. Visualization options cover common needs (charts, pivots) with moderate customization. Focus is on accuracy & consistency over flashy visuals.

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.

Holistics

+ Unified metrics layer: define once in code, use anywhere – similar to LookML but using SQL and a simpler DSL. This ensures a single source of truth across charts.

− Requires data modeling effort upfront; not as plug-and-play. Smaller community (less third-party resources than bigger tools).

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.

Holistics

DB Access: Yes, queries run on your DB. Optionally can cache query results in its warehouse for speed. Virtualization: Holistics does not store data long-term; it queries live or caches in temp tables. Push: No, it's pull (with ability to schedule cache refresh). Other: Supports row-level security definitions in the modeling layer; robust role-based view permissions.

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