A company wants near-real-time analytics using Amazon Kinesis Data Streams and Amazon Redshift, ingesting several gigabytes per second, and leveraging existing BI tools. Which option minimizes operational overhead while making streaming data available for immediate analysis?

The correct option leverages Amazon Redshift's native streaming ingestion capabilities, which directly integrate with Kinesis Data Streams. Creating an external schema and a materialized view with auto-refresh minimizes operational overhead by automating data loading and making data immediately available for analysis without manual intervention or separate ETL jobs. The first incorrect option, using Kinesis Data Streams to stage in S3 and then COPY into Redshift, introduces latency and requires managing the COPY process, which is not near-real-time and increases operational overhead. The second incorrect option, directly querying Kinesis Data Streams with SQL and creating materialized views on the stream, is not a standard or supported Redshift feature for direct stream querying. Redshift requires an ingestion mechanism. The third incorrect option, using Kinesis Data Firehose to stage in S3 and then COPY into Redshift, is similar to the first incorrect option. While Firehose simplifies delivery to S3, it still involves an S3 staging step and the COPY command, adding latency and operational steps compared to Redshift's direct streaming ingestion.