Hello again,

I have a couple of questions regarding Databricks cluster configuration and best practices.

Are there any recommended best practices or guidelines for configuring Databricks clusters (e.g. sizing, cores per executor, memory settings, etc.) depending on the workload?

In on-premise Spark deployments, it is sometimes recommended to leave a certain number of CPU cores or a percentage of CPU/memory reserved for the operating system and the JVM (for example, not allocating 100% of resources to Spark executors).
Is there an equivalent recommendation or consideration in Databricks-managed environments, or is resource management fully handled by the platform?

Thanks in advance for your help.