SPIN notebooks extend the familiar Jupyter model with specialized cell types and operational features for troubleshooting and automation. Understanding these concepts will help you create effective operational runbooks.

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Notebook Fundamentals

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Notebook Structure

A SPIN notebook consists of:
  • Sequential cells: Executed independently or as a workflow
  • Session state: Persists variables and context across cell executions
  • Output history: Captures results for collaboration and audit
  • Metadata: Defines notebook purpose, ownership, and configuration

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Cell Execution Model

Unlike scripts, notebook cells can be:
  • Executed individually for testing and development
  • Run out of order for non-linear troubleshooting
  • Re-executed to refresh data or retry failures
  • Chained together with shared variables and state

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Cell Types

SPIN supports multiple cell types for different tasks: There are also cells for specific integrations: You can also extend SPIN with custom cells using Add-ons.

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Output Variables

Cells can store results in named variables for use by later cells:
  • Variables persist throughout the session
  • Shared across all cell types
  • Complex data structures (DataFrames, JSON) are preserved
  • Variables can be cleared or reset

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Template Variables

Enable dynamic notebook behavior using template variables.

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Output and Results

SPIN provides specialized rendering for operational data:
  • Data Tables
  • Time Series Charts
  • JSON Trees
as well as standard Jupyter outputs:
  • Images
  • HTML
  • Markdown
  • Text