The best solution to minimize the storage cost of the migration of your company's on-premises Apache Hadoop servers to Google Cloud Dataproc is
- ✅ "A. Put the data into Google Cloud Storage".
- ✅ Here's why:
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✅ Cost Efficiency: Google Cloud Storage (GCS) is cheaper compared to Google Persistent Disk, which can significantly reduce storage costs.
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✅ Compatibility with Hadoop: GCS is compatible with Hadoop. It provides a connector that allows Hadoop jobs to use GCS for input and output.
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✅ Durability and Availability: GCS provides high data durability and availability, making it a suitable storage option for large amounts of data.
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✅ Scalability: GCS is highly scalable and can handle large amounts of data, making it suitable for migrating a Hadoop cluster.
- 🔴 Now, let's examine why the other options are not the best choice:
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❌ B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster: Preemptible VMs are cheaper than regular instances, but they are not related to storage cost savings. Moreover, they are short-lived, which may not be suitable for long-running jobs.
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❌ C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data: This option might not significantly reduce the storage cost as the data size is large (50TB per node). Moreover, it can lead to storage capacity issues in the future.
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❌ D. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk: This option could still result in high costs because Persistent Disk is more expensive than GCS. Also, it may lead to complexity in managing data across two storage systems.
