Google Cloud Professional Data Engineer Practice Exam 2025 – Complete Preparation

The recommended storage option for processing large amounts of data with Dataproc is Cloud Storage. This is primarily because Cloud Storage is designed to handle large volumes of unstructured data in a highly scalable and reliable manner. It offers significant advantages for data processing workflows, such as being easily accessible from Dataproc clusters, supporting high throughput and low latency, and providing durability and redundancy.

Cloud Storage is integral to the data lake architecture and enables seamless integration with other Google Cloud services. It is optimized for handling diverse data types and sizes, making it a suitable choice for big data workloads. Additionally, it allows you to take advantage of features like automatic scaling and global accessibility, which are essential when processing extensive datasets in distributed computing environments.

While other options like Cloud SQL, zonal persistent disks, and local SSD may serve specific use cases, they do not match the scalability and flexibility offered by Cloud Storage for handling large datasets efficiently in a big data ecosystem.