A company stores JSON and CSV datasets in S3 and also has Amazon RDS for SQL Server, provisioned-capacity DynamoDB tables, and an Amazon Redshift cluster. Data scientists need to query all these sources using SQL-like syntax with minimal operational overhead. Which solution meets this requirement?

The correct solution leverages AWS Glue Data Catalog as a central metadata repository for all data sources (S3, RDS, DynamoDB, Redshift). Amazon Athena can then query this catalog, providing SQL-like access across diverse data stores, including S3, RDS, and DynamoDB (via Athena federated queries). Athena supports standard SQL for structured data and PartiQL for querying JSON data, meeting the requirement for SQL-like syntax. This approach minimizes operational overhead as Athena is serverless. The second option is incorrect because Redshift Spectrum can query S3 and Redshift, but it cannot directly query RDS or DynamoDB tables in the same way Athena federated queries can. The third option is incorrect because transforming JSON to Parquet or CSV adds unnecessary operational overhead and latency, and it doesn't directly address querying RDS or DynamoDB with Athena. The fourth option is incorrect because while Lake Formation helps build data lakes and secure data, transforming all data to Parquet adds overhead and isn't strictly necessary for querying with SQL-like syntax across all specified sources, especially RDS and DynamoDB.