An ecommerce business has multiple operational systems in AWS, each with a JDBC-compliant relational database storing the latest processing state. The operations team needs hourly visibility into orders across the entire fulfillment workflow. Which approach delivers this with the least development effort?

The correct answer leverages AWS Glue for ETL and Amazon Redshift for analytical querying, which is ideal for hourly reporting across multiple relational databases. AWS Glue can easily connect to JDBC-compliant sources, extract data, transform it, and load it into Redshift, a data warehouse optimized for analytical queries. Amazon QuickSight is a powerful business intelligence service that integrates seamlessly with Redshift to create interactive dashboards for tracking orders. Using Amazon DynamoDB (an incorrect option) is not suitable for complex analytical queries across multiple tables, as it's a NoSQL database optimized for high-performance key-value lookups, not analytical reporting. AWS DMS (another incorrect option) is primarily for database migrations and continuous replication, not for building complex ETL pipelines with transformations needed for analytical reporting. While it can capture changes, it's not designed for the full ETL process that Glue provides.