Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 54 discussion

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.

B. https://aws.amazon.com/jp/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on-amazon-emr/

I will go with A. Besides DMS is typical for migration, it's the only choice which explicitly concerns about how the migration itself will be made. Other choices would demand a script or GLUE ETL job if you will. But this logic of migration was never put

I will go with A

serverless catalog in AWS == glue

B. Set up an AWS Glue ETL job which extracts metadata from your Hive metastore (MySQL) and loads it into your AWS Glue Data Catalog. This method requires an AWS Glue connection to the Hive metastore as a JDBC source. An ETL script is provided to extract metadata from the Hive metastore and write it to AWS Glue Data Catalog. https://github.com/aws-samples/aws-glue-samples/blob/master/utilities/Hive_metastore_migration/README.md

https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on-amazon-emr/ Option B is likely the most suitable. Migrating the Hive metastore into Amazon EMR and using AWS Glue Data Catalog as an external catalog provides a balance between leveraging the scalable and managed services of AWS (like EMR and Glue Data Catalog) and ensuring a smooth transition from the on-premises setup. This approach leverages the serverless nature of AWS Glue Data Catalog, minimizing operational overhead and potentially reducing costs compared to managing database servers.