You need to migrate a 30-node Hadoop cluster to the cloud, reuse existing Hadoop jobs, minimize cluster management, and persist data beyond cluster lifetime. What should you do?

Creating a Cloud Dataproc cluster with the Google Cloud Storage connector is the optimal solution. Dataproc is a managed Hadoop and Spark service, minimizing cluster management overhead while allowing you to reuse existing Hadoop jobs. Utilizing Google Cloud Storage (GCS) as the underlying storage layer ensures data persistence beyond the cluster's lifetime, as GCS is a highly durable and available object storage service. The GCS connector allows Hadoop jobs to seamlessly interact with data stored in GCS. Google Cloud Dataflow is a serverless service for stream and batch processing but requires rewriting existing Hadoop jobs. Creating a Hadoop cluster on Google Compute Engine offers more control but increases management overhead. Using persistent disks for HDFS in Dataproc or Compute Engine means data persistence is tied to the cluster's disks, which is less flexible and scalable than GCS. Local SSDs provide high performance but are ephemeral, meaning data is lost when the instance terminates.