A company bought appliances from multiple vendors. Each appliance has IoT sensors that send status messages in each vendor's proprietary format to a legacy app that parses them into JSON. Parsing is simple but vendor-specific. Once per day, the app parses all JSON records and writes them to a relational database for analysis. The company wants a new data analysis design that is faster and more cost-effective. Which solution meets these requirements?

The correct solution leverages AWS IoT Core for scalable ingestion of sensor data, which is designed for IoT device connectivity. An AWS Lambda function provides a serverless, cost-effective way to parse proprietary formats into a standardized format (CSV) and store them in Amazon S3, an inexpensive and highly durable object storage service. AWS Glue automatically discovers schemas and catalogs the data, making it queryable. Amazon Athena allows direct, serverless querying of data in S3 using standard SQL, and Amazon QuickSight provides interactive dashboards for analysis, offering a faster and more cost-effective solution than the legacy app. Migrating the parsing application to AWS Fargate might improve scalability but still involves managing an application and a relational database, which can be more expensive and less agile than a serverless approach for this use case. Deploying an AWS Transfer for SFTP server and modifying sensors is complex and introduces unnecessary overhead; IoT Core is purpose-built for this. AWS Snowball Edge is for large-scale data transfer and edge computing, not for real-time or near real-time ingestion and analysis of continuous IoT sensor data.