A company needs a centralized data catalog and metadata management for cloud-based data sources (structured sources such as Amazon RDS and Redshift, and semistructured files in S3). The catalog must be refreshed regularly and detect metadata changes with minimal operational overhead. Which solution meets these requirements?

The AWS Glue Data Catalog is a fully managed, centralized metadata repository designed for various data sources, including Amazon RDS, Redshift, and S3. AWS Glue crawlers automatically connect to these data stores, infer schemas, and populate the Data Catalog with metadata. They can detect schema changes and update the catalog, providing minimal operational overhead. Scheduling crawlers ensures regular refreshes. Incorrect options: Using Amazon Aurora or DynamoDB as a data catalog with Lambda functions would require significant custom development and maintenance for schema inference, change detection, and integration with diverse data sources, increasing operational overhead. These services are not designed as native metadata catalogs. While the AWS Glue Data Catalog is correct, manually extracting schemas for RDS and Redshift defeats the purpose of Glue crawlers' automation for these sources. Crawlers can infer schemas for structured databases as well as S3, making the manual step unnecessary and less efficient.