Exploring Qlik Sense, Project Jupyter & Visivo

Qlik Sense Vs. Project Jupyter Vs. Visivo

In this article, we'll compare the key features, capabilities, and differentiators between Qlik Sense, 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.

FeatureQlik SenseProject JupyterVisivo
Deployment ModelClient-managed (Windows), Client-managed (Kubernetes), Qlik Cloud (SaaS)Open-source (local), Jupyter server, JupyterHub deploymentOpen-source, Cloud Service, Self-hosted
PricingCommercial. Qlik Sense Enterprise is subscription (by user or capacity). Qlik Sense Desktop free for personal use.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.

ToolDeployment ModelPricingCost
Qlik SenseClient-managed (Windows), Client-managed (Kubernetes), Qlik Cloud (SaaS)Commercial. Qlik Sense Enterprise is subscription (by user or capacity). Qlik Sense Desktop free for personal use.$$$$
Project JupyterOpen-source (local), Jupyter server, JupyterHub deploymentFree (BSD license)$
VisivoOpen-source, Cloud Service, Self-hostedOpen 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.

Qlik Sense

Enterprise BI usersData analystsGoverned self-service users

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.

Qlik Sense

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.

Qlik Sense

Multiple data sources via connectorsPython/R Server Side ExtensionsWeb APIs and databases

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.

ToolAI Features
Qlik Sense
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.

Qlik Sense

Powerful interactive visuals with unique associative filtering: all charts update with each selection, and show gray 'excluded' values to encourage discovery. Chart types cover most needs, and extension mechanism allows custom charts. Good formatting control but primarily via UI (no raw HTML/CSS editing).

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.

Qlik Sense

+ The Associative Engine is Qlik's hallmark – users can freely explore data without query limitations (no SQL needed when using the app). Great for uncovering relationships in data.
− Requires data to be loaded into memory for full power, which can be heavy. Learning Qlik's script and the "set analysis" syntax for expressions has a learning curve.

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.

Qlik Sense

DB Access: Typically Qlik imports data into its engine (so not needed at runtime). The new Direct Query option (in Qlik Cloud) allows leaving data in DB and querying on interaction for huge data sets. Virtualization: Qlik's standard method is not virtualization but in-memory copies. However, with Direct Query it behaves virtually. Push: Data is pushed into Qlik via load scripts (you schedule reloads). Other: Strong security – Section Access to implement row-level security inside Qlik apps, and robust user access control on the server.

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.

$ curl -fsSL https://visivo.sh | bash
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Jared Jesionek (co-founder)
Jared Jesionek (co-founder)
Jared Jesionek (co-founder)
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How can I help? This connects to our slack so I'll respond real quickly 😄
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