AWS Glue Data Catalog for Least Overhead Metadata Management

Understand data cataloging systems.

Answer Correct answer: B — Use the AWS Glue Data Catalog as the central metadata repository and use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes.

A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3. The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata. Which solution will meet these requirements with the LEAST operational overhead?

Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog. Correct Answer
Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Community Votes

100%

100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests the selection of a managed metadata service versus custom solutions, with the common trap being the creation of custom Lambda functions or databases (like Aurora/DynamoDB) which introduce significant operational overhead.

The AWS Glue Data Catalog is the recommended service for centralized metadata management across diverse data stores with minimal operational overhead. This page explains why using AWS Glue crawlers satisfies the requirements for automated, regular updates to structured and semi-structured data sources.

Learners often choose Option A or C because they involve coding (Lambda), underestimating the maintenance burden compared to a fully managed crawler service. Option D is incorrect because it mixes manual schema extraction with automation, failing the 'least operational overhead' criterion.

Community Discussion (5 comments)

pypelyncar 👍 7 Selected: B

The AWS Glue Data Catalog is a purpose-built, fully managed service designed to serve as a central metadata repository for your data sources. It provides a unified view of your data across various sources, including structured databases (like Amazon RDS and Amazon Redshift) and semi-structured data formats (like JSON and XML files in Amazon S3).

valuedate 👍 3 Selected: B

glue data catalog with crawlers

hnk 👍 1 Selected: A

B is the obvious answer

GiorgioGss 👍 4 Selected: B

A,C out for obvious reason D out because it involves manual schema extract

rralucard_ 👍 3 Selected: B

Option B, using the AWS Glue Data Catalog with AWS Glue Crawlers, is the best solution to meet the requirements with the least operational overhead. It provides a fully managed, integrated solution for cataloging both structured and semistructured data across various AWS data stores without the need for extensive manual configuration or custom coding.

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Expert Analysis

Why the Answer Is Correct

Option B is correct because the AWS Glue Data Catalog is a purpose-built, fully managed metadata repository that integrates natively with both relational databases (RDS, Redshift) and object storage (S3). By using AWS Glue crawlers, you can automatically discover schemas and update metadata on a schedule without writing code or managing infrastructure. This directly addresses the requirement for 'least operational overhead' while supporting the necessary detection of changes.

Why the Other Options Are Wrong

Options A and C suggest using Amazon Aurora or DynamoDB as a data catalog. These are database services, not metadata catalogs; implementing a catalog on top of them requires building custom logic with Lambda functions, which creates high operational overhead. Option D involves manually extracting schemas for RDS and Redshift, which contradicts the goal of minimizing operational effort and does not provide the automated change detection for those specific sources that crawlers offer.

Community Comment Notes

Community consensus strongly favors Option B, with users noting that options involving custom code (A, C) are overly complex and option D is flawed due to manual steps. As user pypelyncar noted, the Glue Data Catalog is designed specifically for this unified view. Another user highlighted that options A and C are eliminated for obvious reasons, and D is out because it involves manual schema extraction.

Exam Strategy

When asked for 'least operational overhead' in AWS architecture questions, prioritize fully managed services over custom implementations using compute resources like EC2 or Lambda. Look for keywords like 'automatically', 'integrated', and 'managed' in the correct option's description.

Frequently Asked Questions

Why not use Aurora or DynamoDB as a data catalog?

Aurora and DynamoDB are database services, not metadata catalogs. Using them would require building custom applications to manage metadata, increasing operational overhead significantly.

Can AWS Glue Crawlers handle semi-structured data like JSON?

Yes, AWS Glue Crawlers can infer schemas from semi-structured data formats such as JSON, XML, and Parquet stored in Amazon S3 and update the Data Catalog automatically.

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