AWS Certified Data Engineer Associate DEA-C01 Practice Question
An analytics team is setting up a new Amazon S3 data lake and needs a centralized catalog so analysts can query the data with Amazon Athena and Amazon Redshift Spectrum. The catalog should automatically discover schemas and partitions from S3 and require no infrastructure management. Which approach is the most cost-effective solution?
Use AWS Lake Formation to create resource links for the S3 location and configure an external Apache Ranger catalog for metadata storage.
Deploy an Apache Hive metastore on an Amazon EMR cluster and use HCatalog to register the S3 data.
Store schema information in an Amazon DynamoDB table and build AWS Lambda functions to update partition metadata.
Create an AWS Glue crawler for the S3 bucket and use the AWS Glue Data Catalog as the metastore for Athena and Redshift Spectrum.
Using the AWS Glue Data Catalog with an AWS Glue crawler directly addresses all requirements. The Data Catalog is a fully managed, serverless metadata repository, so there is no infrastructure to provision or maintain. A crawler can be scheduled to scan the S3 bucket, infer table schemas, detect new partitions, and register the results in the catalog, making the data immediately accessible from Athena and Redshift Spectrum. Running an Apache Hive metastore on EMR would require maintaining a cluster and paying for EC2 instances. Storing schema details in DynamoDB would demand custom code and does not provide native integration with Athena. Lake Formation still relies on the Glue Data Catalog; adding an external Apache Ranger catalog introduces unnecessary complexity and cost.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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