You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate

Manually creating a VM instance and installing all Spark dependencies is significantly higher effort and generally not recommended as a first step for a managed cloud environment PoC when managed services are available.

Setting up Spark on a Google Kubernetes Engine (GKE) cluster is a complex task requiring deep Kubernetes knowledge and is not suitable for a 'minimal effort' initial proof of concept.

While creating a Dataproc cluster and a Vertex AI Workbench notebook is an excellent solution for running PySpark at scale, it is a higher initial cost and effort than simply starting with a Workbench notebook for a proof of concept of a single job.

For a proof of concept requiring minimal cost and effort to migrate a PySpark job, creating a Vertex AI Workbench notebook is the most straightforward first step. Workbench provides a managed Jupyter environment that can quickly be used to test PySpark code, potentially running in a local standalone Spark mode on the notebook instance, before scaling out to a full Dataproc cluster.