modules
Computational Notebooks
Data analysis that lives inside your knowledge documents. Reactive notebooks where cells automatically recalculate when their data changes — supporting Python, R, and Julia, with live connections to your enterprise data sources.
Data analysis that lives inside your knowledge documents. Reactive notebooks where cells automatically recalculate when their data changes — supporting Python, R, and Julia, with live connections to your enterprise data sources.
analysis-that-stays-current
Analysis That Stays Current Traditional notebooks have a dirty secret: hidden state. Cells depend on execution order, and results go stale the moment upstream data changes. MUKR's notebooks are reactive — change a cell, and every dependent cell recalculates automatically. The dependency graph is explicit and visual. Even better: notebooks bind directly to your enterprise data sources. Reference a dataset, and the notebook pulls live data. When the upstream source updates, your analysis refreshes. No more copying CSVs into Jupyter and hoping the numbers are current.
data Viz
Notebook Capabilities
- Languages
- Python 3.11+, R 4.3+, Julia 1.9+
- Execution Model
- Reactive — dependencies recalculate automatically
- Data Binding
- Live connections to enterprise datasets
- Output Types
- Tables, charts, maps, 3D visualization, LaTeX, HTML
- Collaboration
- Real-time multi-user editing of shared notebooks
- Reproducibility
- Locked dependency versions with full execution traces
- Memory
- Configurable per kernel, default 8GB
features
engine: Marimo
reactive: true
reproducible: true
reference binding: true
Section 1
Python
Section 2
R
Section 3
Julia
Relationships
products
industries
capabilities
sub modules
use cases
- Computational Notebooks
- Epistemological Knowledge
business sizes
- Enterprise
- Government
- Mid-Market