Answer-first summary for fast verification
Answer: Migrate the workloads to Dataproc plus Cloud Storage; modernize later.
Migrating the workloads to Dataproc plus Cloud Storage is the best initial approach. Dataproc, a managed Spark and Hadoop service on Google Cloud, allows for a seamless migration of existing Spark, Hive, and HDFS workloads without significant changes. Cloud Storage serves as a scalable and cost-effective replacement for HDFS. This strategy enables a quick migration within the 2-month timeframe, reducing on-premises overhead and maintenance costs, and allows for gradual modernization to serverless offerings like BigQuery and Dataflow post-migration. - **Option A** suggests immediate modernization, which may not be feasible within the tight timeframe. - **Option C** partially modernizes but retains HDFS, missing out on Cloud Storage's cost benefits. - **Option D** uses HDFS with Dataproc, which is less cost-effective and scalable than Cloud Storage.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
Your company operates a large on-premises cluster with Spark, Hive, and HDFS in a colocation facility, designed for peak usage but experiences fluctuating demand due to batch jobs. Aiming to migrate to the cloud to reduce overhead and modernize with serverless offerings, with only 2 months before their colocation contract renewal, how should they approach their migration strategy to maximize cost savings and ensure timely migration?
A
Modernize the Spark workload for Dataflow and the Hive workload for BigQuery.
B
Migrate the workloads to Dataproc plus Cloud Storage; modernize later.
C
Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
D
Migrate the workloads to Dataproc plus HDFS; modernize later.
No comments yet.