What is a common use for worker nodes in a Databricks cluster?

Worker nodes play a critical role in the architecture of a Databricks cluster, particularly in the context of running data processing tasks. They are primarily responsible for executing workloads, which include running Spark executors that are essential for processing data in a distributed manner. When a Spark job is submitted, the tasks associated with this job are distributed across the available worker nodes, allowing them to process data in parallel. This design enables efficient resource utilization and significant speedups for large-scale data processing tasks.

Moreover, worker nodes might also be tasked with running other services that support data processing within the Databricks environment. This includes executing transformations and actions on the data residing in various data sources. By leveraging the multiple worker nodes, Databricks can effectively handle large datasets, ensuring scalability and performance for complex analytical tasks.

In contrast, managing cluster permissions is typically the role of the driver or the cluster manager rather than the worker nodes themselves. While worker nodes have access to data, their primary function is not to store large datasets but rather to process and analyze the data that is made available to them. Finally, while worker nodes can execute Spark tasks, they also do not focus solely on non-Spark commands but are optimized for running distributed data processing jobs, particularly those