Introduction

Syntasa Notebooks are JupyterLab-based notebooks integrated into the Syntasa platform. You write cells in Python or Scala, run them against Spark, share notebooks across a workspace, schedule them as jobs, and lean on the platform for everything underneath: managed kernels, scoped credentials, shared cluster compute, and a unified utility namespace that makes connections, datasets, secrets, and storage one-line operations.

If you've used Jupyter before, the editing experience here is mostly familiar. What this guide covers is the layer the platform adds on top — workspaces, runtime templates, init scripts, the security model, and the operational surfaces (logs viewer, Spark UI, History Server) that make notebooks pleasant to work in at scale.

The three notebook flows

There are three ways a notebook runs on Syntasa, and almost everything else in this guide is described in terms of one of them. Knowing them upfront makes the rest of the guide easier to navigate.

The three notebook flows. Pick the one that fits the work you're doing.
The three notebook flows. Pick the one that fits the work you're doing.
  • Interactive workspace — open a notebook, run cells. The default kernel handles small interactive work without any setup. Notebook Workspaces covers workspaces; Real-Time Collaboration and Spark Session Information cover the interactive editing experience.
  • Interactive + attached runtime — start interactive, then attach a runtime template when you need a bigger driver, more executors, GPU access, or pre-configured Spark settings. Runtime Attachment in Notebook Workspaces covers runtime attachment end-to-end.
  • Notebook Process (Jobs) — run a notebook unattended, on a schedule or on demand. UnderReview_Notebook Process (Jobs) covers configuration, parameters, and the differences from the interactive flows.