DAS-C01 · Question #127
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the…
The correct answer is B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to. AWS Glue Data Catalog is a fully managed, serverless metadata repository that natively supports structured sources (Amazon RDS, Amazon Redshift) and semi-structured sources (JSON, XML in S3). AWS Glue crawlers automatically connect to data stores, infer schemas, detect schema…
Question
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration. Which solution meets these requirements?
Options
- AUse Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and
- BUse the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to
- CUse Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect
- DUse the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS
How the community answered
(40 responses)- A8% (3)
- B73% (29)
- C5% (2)
- D15% (6)
Explanation
AWS Glue Data Catalog is a fully managed, serverless metadata repository that natively supports structured sources (Amazon RDS, Amazon Redshift) and semi-structured sources (JSON, XML in S3). AWS Glue crawlers automatically connect to data stores, infer schemas, detect schema changes, and update the catalog on a configurable schedule - all with minimal administration. Option A (Aurora + Lambda) and option C (DynamoDB + Lambda) require significant custom development to build crawler logic, schema detection, and change tracking, dramatically increasing administration overhead. Option D uses Glue Data Catalog but relies on manual schema extraction for RDS and Amazon Redshift, losing the automated change detection benefit of crawlers.
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