A healthcare firm streams real-time telemetry from devices, equipment, and records into Amazon Kinesis Data Streams. The data must be stored in an Amazon Redshift Serverless warehouse and support near-real-time analytics plus queries on the previous day's data. Which solution delivers this with the least operational overhead?

The streaming ingestion feature of Amazon Redshift Serverless directly loads data from Amazon Kinesis Data Streams into Redshift tables. This provides near-real-time analytics capabilities and supports queries on historical data efficiently with minimal operational overhead, as it's a native Redshift feature. Loading data into Kinesis Data Firehose and then Redshift adds an unnecessary intermediate service and increases latency compared to direct streaming ingestion. Loading data into Amazon S3 and using the COPY command is suitable for batch loading, not near-real-time streaming. Amazon Aurora zero-ETL integration with Amazon Redshift is for data originating in Aurora, not Kinesis Data Streams.