To run a PySpark batch data pipeline without managing cluster resources, what should you configure?

Configuring Dataproc Serverless is the optimal choice for running a PySpark batch data pipeline without the need to manage cluster resources. Dataproc Serverless allows users to execute Spark applications on a temporary computing environment that dynamically allocates resources as needed. This means you do not have to provision, manage, or scale a cluster; instead, you can focus on your data processing tasks.

By using Dataproc Serverless, you benefit from a fully managed service that automatically optimizes resources for your workload. The serverless model provides on-demand scalability, reducing the overhead and complexity associated with cluster management. It is particularly advantageous for batch jobs, where resource requirements may fluctuate, making it more efficient than using a standard cluster that remains up even when idle or underutilized.

Dataproc Serverless is designed specifically to address use cases like this: allowing you to run workloads while abstracting away the underlying infrastructure. This not only streamlines operations but can also lead to cost savings since you pay only for the resources consumed during the job execution.