Accompany is building a system to collect sensor data from hundreds of factory devices. Devices publish data every 5 seconds to AWS IoT Core. The data must be enriched before it is loaded into an Amazon S3 data lake and new sensor data must be available in S3 within 30 minutes of collection. No other applications consume the IoT Core messages. Which solution is the MOST cost-effective while meeting these requirements?

The correct solution uses AWS IoT Core Basic Ingest, which is more cost-effective than standard ingest because it bypasses the message broker for direct rule processing. Kinesis Data Firehose is ideal for loading streaming data into S3, and its built-in data transformation with Lambda allows for enrichment before delivery. Setting the buffering interval to 900 seconds (15 minutes) ensures data is delivered to S3 well within the 30-minute requirement while optimizing for cost by batching. The first incorrect option incurs higher costs due to standard IoT Core ingest and invokes Lambda for every message, which is inefficient for high-volume, low-latency data. The third option adds unnecessary cost and complexity by introducing Amazon Timestream, which is not required given the enrichment and S3 storage needs. The fourth option uses Kinesis Data Streams, which is more expensive and complex to manage for this use case than Kinesis Data Firehose, especially when the primary destination is S3.