The guide is split into six parts:
- Working with Notebooks : the workspace model, real-time collaboration, and the difference between notebook cards and JupyterLab notebooks.
- Compute & Runtimes : runtime templates, the Spark session info display, and Notebook Process jobs.
- Customization : the init scripts and dependency model, plus the synutils namespace for everything from credentials to Spark DataFrames.
- Observability & Logs : kernel logs viewer, Spark UI, History Server, and the deeper troubleshooting reference.
- Security & Access Control : Data Authorization (Spark engine), Session Policy (AWS credentials), and how the two layers work together.
- Advanced Cell Magics : the %run magic for chaining notebooks, plus a brief note on capabilities in development.