When migrating from an on-premises Hadoop environment, which approach minimizes changes to existing jobs and architecture?

Creating a Cloud Dataproc cluster on Google Cloud Platform is the approach that minimizes changes to existing jobs and architecture when migrating from an on-premises Hadoop environment. Cloud Dataproc is a fully managed service that allows you to run Apache Hadoop and Apache Spark jobs in the cloud. It is designed to be compatible with existing Hadoop environments, which means that your existing jobs, code, and workflows can be deployed with minimal modifications.

By using Cloud Dataproc, you can instantiate a cluster that closely mirrors your on-premises setup, utilizing familiar tools and configurations. This helps maintain operational continuity, allowing your development teams to leverage their existing knowledge and skills without needing to learn new systems or architectures.

The option of moving HDFS data to larger disks might imply some additional steps or changes in how data is managed, as you're shifting not just the computation environment but also the storage architecture. Although this could be beneficial in some contexts, it can complicate the migration process if significant changes are needed. The primary goal is to keep the architecture as unchanged as possible while still utilizing cloud capabilities effectively.

In summary, creating a Cloud Dataproc cluster enables a more straightforward transition and allows you to leverage your current investment in Hadoop tools and processes while benefiting from the