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Answer: Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.
The company's main concerns are reducing storage costs, minimizing maintenance, and avoiding major changes to existing jobs. Option D addresses these by migrating to Cloud Dataproc (managed Hadoop/Spark) and moving HDFS data to Cloud Storage (lower cost, managed storage). The Cloud Dataproc connector allows jobs to access Cloud Storage data as if it were HDFS, requiring minimal code changes. Other options either retain HDFS (C, B) or require significant job rewrites (A).
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How can your company migrate an on-premises Hadoop environment to the cloud while minimizing changes to existing data analytics jobs and architecture, given concerns about rising HDFS storage costs and maintenance?
A
Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.
B
Create Compute Engine instances with HDD instead of SSD to save costs. Then perform a full migration of your existing environment into the new one in Compute Engine instances.
C
Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop environment to the new Cloud Dataproc cluster. Move your HDFS data into larger HDD disks to save on storage costs.
D
Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.
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