Final answer:
Cloud Dataproc allows for separation of compute and storage by enabling Spark programs to leverage cloud storage services for data while independently scaling compute resources.
Step-by-step explanation:
Cloud Dataproc allows Spark programs to separate compute and storage, which means that compute resources can scale independently from storage resources. This separation is facilitated by leveraging cloud storage services, such as Gogle Cloud Storage, to house the data, while the compute nodes of Cloud Dataproc process the data.
It aligns with the broader trend of decoupling resources in cloud computing, enabling better scalability and potentially more cost-effective use of resources. For example, you can shut down a Dataproc cluster after processing your data without affecting the data itself, which is safely stored in Gogle Cloud Storage.
Hence, Cloud Dataproc enables the decoupling of compute and storage, allowing Spark programs to utilize cloud storage services for data while independently adjusting compute resources to scale efficiently.