Compute and Runtimes
Optimized Spark Defaults
Your Spark jobs are now sized from the actual capacity of your cluster's nodes, and start with a set of Spark settings chosen for the Syntasa platform rather than Spark's generic defaults. It is on by default, and every setting it applies can still be overridden from your runtime template.
Spark Right Sizing and Notebook Modes
Kubernetes Spark jobs and notebooks used to run on one-size-fits-nothing defaults unless you hand-tuned every process. The platform now reads the actual hardware, sizes the job for you, and applies the tuning most teams end up adding by hand — and your own settings still win everywhere.
Runtime Attachment in Notebook Workspaces
Most notebook work is fine on the default kernel — you don't have to attach anything to start. Attach a runtime when you outgrow the default: for example, when you need a bigger driver, more executors, GPU access, or a runtime that has been pre-configured with specific Spark settings and…
Spark Session Information
Type spark in a notebook cell and run it. Both Python and Scala kernels render a small HTML block summarizing the current Spark session — it is the _repr_html_ of the SparkSession object. Useful for confirming what your kernel is connected to, especially after attaching or detaching a runtime.
Notebook Process (Jobs)
A Notebook Process runs a notebook non-interactively as part of a Syntasa workflow. It is one process type among many — alongside Spark code processes, app processes, and so on — and uses the same job orchestration model. Manual runs, scheduled runs, aborting, run history: all of it works the same…