How can you ensure data consistency while optimizing costs for running stateful batch jobs on GKE using pre-emptible instances, especially when these jobs depend on a shared dataset?

*To achieve cost optimization while maintaining data consistency for stateful batch jobs on GKE using pre-emptible instances, the recommended solution is to use GKE with pre-emptible nodes, complemented by Persistent Disks configured with the ReadWriteMany access mode for the shared dataset. GKE's integration with pre-emptible nodes offers significant cost savings, as these instances are less expensive than standard ones. However, since pre-emptible instances can be terminated at any time, using Persistent Disks with ReadWriteMany access mode ensures that the shared dataset remains consistent and accessible across different job instances. This setup balances cost-efficiency with the stateful nature of the batch jobs, ensuring data integrity and availability even in the event of instance preemption. Other options, such as using Dataflow with Cloud Bigtable or Dataprep with standard VMs, may either not provide the necessary cost benefits or fail to adequately address the stateful and shared nature of the dataset in a pre-emptible environment. Dataproc with HDFS and Cloud Run do not align with the requirement for pre-emptible instances and shared state management.*