Exploring Looker Studio, Project Jupyter & Visivo

Looker Studio vs. Project Jupyter vs. Visivo

Compare key features, capabilities, and differentiators between Looker Studio, 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	Looker Studio	Project Jupyter	Visivo
Deployment Model	Cloud (Google Cloud), Enterprise deployment, Private cloud	Open-source (local), Jupyter server, JupyterHub deployment	Open-source, Cloud Service, Self-hosted
Pricing	Free to use (with Google account); Pro version for enterprise (Looker Studio Pro) introduced with SLAs.	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.

Looker Studio

Business usersMarketersGoogle ecosystem users

Project Jupyter

Data scientistsResearchersEngineers

Visivo

Analytics EngineersData teamsBusiness usersEngineers

Ease of Development & Deployment

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

Looker Studio

2/5

Project Jupyter

2/5

Visivo

5/5

Key Integrations & Ecosystem

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

Looker Studio

500+ data connectorsGoogle products (Analytics, Ads)SQL databases via Simba drivers

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.

Looker Studio

Drag-and-drop report editor. Offers charts like time series, bar, geo maps, tables. Customization is decent (colors, labels), though not as fine-grained as Tableau. Supports community visualizations (bring custom JS charts). Layout is canvas-style – good for dashboards and infographics.

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.

Looker Studio

Completely free for most use-cases. Extremely easy for simple needs – non-tech users can create a shareable dashboard in minutes. Being Google, sharing and embedding is seamless.

Lacks advanced analytics (no calculated fields beyond basic formulas, limited data shaping). Performance can suffer on large data sets unless using aggregated extracts. No row-level security (one report = one set of credentials or extracted data).

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.

Looker Studio

DB Access: Yes, live connects to sources using provided credentials (or OAuth tokens). Option to cache query results in Google's cache for performance. Virtualization: Data remains in source or cache – Data Studio doesn't store data persistently (except cached). Push: No, it pulls data when rendering charts. Other: Uses Google account auth for access; you can manage view/edit permissions on reports. Lacks fine security on data level (you'd need separate reports or filters per audience).

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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