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AWS Certified Data Engineer - Associate · AWS
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A and D can be discarded because of added steps. This link provides documentation for this exact use case : https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on-amazon-emr/ C is also discarded because of the serverless key word, although Aurora can be serverless it is not specified in the choice.
Serverless and Cost-Efficient: AWS Glue Data Catalog offers a serverless metadata repository, reducing operational overhead and making it cost-effective. Using it as an external data catalog means you don't have to manage additional database infrastructure. Seamless Migration: Migrating your existing Hive metastore to Amazon EMR ensures compatibility with your current Hadoop setup. EMR is designed to run Hadoop workloads, facilitating this process. Flexibility: An external data catalog in AWS Glue offers flexibility and separation of concerns. Your metastore remains managed by EMR for your Hadoop workloads, while Glue provides a centralized catalog for broader AWS data sources.
B is answer! By leveraging AWS Glue Data Catalog as an external data catalog and migrating the existing Hive metastore into Amazon EMR, the company can achieve a serverless, persistent, and cost-effective solution for storing and managing their data catalog.