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Answer: Migrate the workloads to Dataproc plus Cloud Storage; modernize later: This strategy offers a balanced approach, enabling timely migration with Cloud Storage for immediate cost benefits, deferring modernization for a later phase.
Given the company's primary constraints of a 2-month deadline and the desire for cost savings, the optimal strategy involves a 'lift and shift' approach to Dataproc with Cloud Storage. This method ensures a swift migration within the deadline while leveraging Cloud Storage for cost efficiency by separating storage and compute, allowing for independent scaling. Full modernization, though advantageous, is time-consuming and best postponed until after the initial migration to meet the urgent timeline.
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A company is transitioning from an on-premises cluster utilizing Spark, Hive, and HDFS to the cloud to leverage cost savings and modernize their infrastructure. With a tight deadline of 2 months for the initial migration due to their colocation facility contract renewal, which migration strategy should they adopt to ensure cost savings while adhering to the timeline?
A
Modernize the Spark workload for Dataflow and the Hive workload for BigQuery: This approach focuses on full modernization, which, while beneficial in the long term, may not be feasible within the constrained timeline.
B
Migrate the workloads to Dataproc plus Cloud Storage; modernize later: This strategy offers a balanced approach, enabling timely migration with Cloud Storage for immediate cost benefits, deferring modernization for a later phase.
C
Migrate the Spark workload to Dataproc plus HDFS and modernize the Hive workload for BigQuery: Combining 'lift and shift' with partial modernization, this option may risk exceeding the 2-month deadline due to the modernization component.
D
Migrate the workloads to Dataproc plus HDFS; modernize later: Although this 'lift and shift' method is quick, it lacks immediate cost savings as it continues to rely on HDFS, requiring managed disks and compute resources.