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Answer: Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
Option B is the correct answer. Using Dataproc allows you to migrate your Apache Hadoop clusters to Google Cloud, which reduces significant infrastructure management overhead compared to on-premises deployments. Using Cloud Storage to handle HDFS use cases provides a scalable and cost-effective storage solution. Cloud Composer, which is built on Apache Airflow, allows you to continue orchestrating your ETL pipelines with minimal changes, maintaining your current workflow processes.
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Your organization is in the process of updating its current on-premises data strategy. Currently, the infrastructure includes: • Apache Hadoop clusters that process multiple large datasets with on-premises Hadoop Distributed File System (HDFS) for data replication. • Apache Airflow that orchestrates hundreds of ETL pipelines with thousands of job steps.
You need to establish a new architecture in Google Cloud capable of managing your existing Hadoop workloads while necessitating minimal alterations to your current orchestration processes. What should you do?
A
Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
B
Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
C
Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
D
Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
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