> ## Documentation Index
> Fetch the complete documentation index at: https://docs.planasonix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Notebooks overview

> Run interactive notebooks within your data pipelines.

**Notebooks** bring exploratory analysis and operational diagnostics into Planasonix so you can prototype transforms, validate assumptions, and document findings next to the pipelines that productionize the work.

## Enterprise feature

Hosted notebooks with workspace credentials and pipeline integration require an **Enterprise** subscription. Other tiers may allow export-only workflows or local Jupyter without in-product execution—check your navigation and [Plans and pricing](/settings/plans-and-pricing).

## Jupyter-style notebooks

Planasonix notebooks support familiar **cells** (code, markdown), **kernel** selection, and **variable** inspection. You connect to approved warehouse or Spark clusters using connections admins provision—no personal passwords embedded in notebook JSON.

<Tabs>
  <Tab title="Exploration">
    Profile samples, chart distributions, and test join logic before you commit nodes on the canvas.
  </Tab>

  <Tab title="Documentation">
    Mix markdown explanations with runnable code so handoffs survive team changes.
  </Tab>
</Tabs>

## Pipeline integration

Notebooks can reference pipeline **parameters** and **run context** when your deployment wires them together. Typical patterns:

* Read the same parameters a nightly job uses to filter partitions during investigation
* Query staging tables a pipeline just wrote without copying connection strings manually

<Info>
  Notebook outputs may contain PII from production samples—apply masking cells and clearance rules consistent with your data classification policy.
</Info>

## Next steps

<CardGroup cols={2}>
  <Card title="Getting started" icon="rocket" href="/notebooks/getting-started">
    Create and run your first notebook.
  </Card>

  <Card title="Spark integration" icon="bolt" href="/notebooks/spark-integration">
    Use Spark and Databricks compute.
  </Card>
</CardGroup>