A company records millions of orders daily in Amazon DynamoDB. New orders are added continuously as customers submit them. A data scientist must produce peak-time predictions and create an Amazon QuickSight dashboard that shows near real-time order data. Which approach minimizes the time between a new order being written to DynamoDB and QuickSight being able to access that new data?

The correct approach is to use Amazon Kinesis Data Firehose to deliver DynamoDB change data to Amazon S3, and configure QuickSight to read the S3 data. DynamoDB Streams can capture item-level changes in a DynamoDB table. Kinesis Data Firehose can then consume these streams and efficiently deliver the data to Amazon S3 with minimal latency, making it available for QuickSight. This provides a near real-time data pipeline. Using AWS Glue to export DynamoDB data to S3 would involve batch processing, introducing significant latency. Sending DynamoDB changes into Kinesis Data Streams and then to S3 is a valid approach, but Kinesis Data Firehose is specifically designed for simplified, fully managed delivery to destinations like S3, often requiring less configuration and operational overhead for this specific use case compared to managing Kinesis Data Streams directly for simple S3 delivery. Having QuickSight call an API to read directly from DynamoDB is generally not scalable or efficient for large, continuously updating datasets and can quickly hit DynamoDB read capacity limits, making it unsuitable for near real-time dashboards on millions of orders.