Migrate an on-prem Hadoop environment to GCP while minimizing job changes and storage cost. What approach should you take?

Creating a Cloud Dataproc cluster allows you to run existing Hadoop jobs with minimal changes, as Dataproc is a managed Apache Hadoop and Spark service. Moving data to Cloud Storage decouples storage from compute, offering highly durable, scalable, and cost-effective object storage, which directly addresses the goal of minimizing storage cost. The Cloud Dataproc connector enables seamless access to data in Cloud Storage from your Dataproc jobs. Migrating to BigQuery would require significant job rewrites, as BigQuery is a data warehouse, not a Hadoop-compatible file system. Lift-and-shifting to Compute Engine VMs with HDDs would not leverage managed services, increasing operational overhead and potentially not optimizing storage costs as effectively as Cloud Storage. Moving HDFS data onto larger HDD disks within Dataproc still couples storage to compute and doesn't offer the same cost benefits or scalability as Cloud Storage.